Paper Citation Record · LEDGER

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices

As of 22 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2501.14172.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.14172 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:40:54.730050Z

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A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T13:40:56.448032Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation d9e4e058-8b1f-4159-83d4-021170764ef9 · outbound

This paper cites an unresolved cited work.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 1

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Observation 5a4d15c5-018c-4fca-aaf6-91073e9a6b67 · outbound

This paper cites Burden of malaria in Ethiopia, 2000– 2016: findings from the Global Health Estimates 2016.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Burden of malaria in Ethiopia, 2000– 2016: findings from the Global Health Estimates 2016

Reference 2

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Observation 34bc7269-07f0-45bf-a9d4-90239cdcaf5d · outbound

This paper cites an unresolved cited work.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 3

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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 4

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Observation 8ed0f540-61e1-408c-8b7f-138c591eaf09 · outbound

This paper cites Deep learning- enabled medical comp uter vision.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Deep learning- enabled medical comp uter vision

Reference 5

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Observation f4fc5bc5-e889-447f-933c-e2483516dfa6 · outbound

This paper cites Computer-aided diagnosis based on extreme learning machine: a review.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Computer-aided diagnosis based on extreme learning machine: a review

Reference 6

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Observation b157c524-5baf-4013-8446-506919473f79 · outbound

This paper cites A novel shallow convnet-18 for malaria parasite detection in thin blood smear images: Cnn based malaria parasite detection.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A novel shallow convnet-18 for malaria parasite detection in thin blood smear images: Cnn based malaria parasite detection

Reference 8

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Observation 822ddfe2-ae8c-4963-bab5-c4351b6098ef · outbound

This paper cites A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models

Reference 9

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Observation 666ea4af-6a63-42cc-a93e-08d61eacabcf · outbound

This paper cites Pre -trained deep convolutional neural network for detecting malaria on the human blood smear images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Pre -trained deep convolutional neural network for detecting malaria on the human blood smear images

Reference 10

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Observation 60087f3f-992d-4f46-8201-8369ba40d59a · outbound

This paper cites Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning

Reference 11

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Observation 30207fac-b0d2-4380-aa55-b9122655e49b · outbound

This paper cites Plasmodium Life Cycle-Stage Classification on Thick Blood Smear Microscopy Images using Deep Learning: A Contribution to Malaria Diagnosis.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Plasmodium Life Cycle-Stage Classification on Thick Blood Smear Microscopy Images using Deep Learning: A Contribution to Malaria Diagnosis

Reference 12

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Observation d041709a-8d7a-4802-9bd6-61ec0b298902 · outbound

This paper cites Texture analysis to detect malaria tropica in blood smears image using support vector machine.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Texture analysis to detect malaria tropica in blood smears image using support vector machine

Reference 13

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Observation 0165e598-257b-461d-a1bd-9dcede073f0a · outbound

This paper cites Detection of peripheral malarial parasites in blood smears using deep learning models.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Detection of peripheral malarial parasites in blood smears using deep learning models

Reference 14

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Observation 768dfd4b-ee6c-4ea2-8d67-d30b7c8c523a · outbound

This paper cites Detection and classification of peripheral plasmodium parasites in blood smears using filters and machine learning algorithms.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Detection and classification of peripheral plasmodium parasites in blood smears using filters and machine learning algorithms

Reference 15

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Observation 35181740-3f1e-4c8e-88a3-e63fc929017f · outbound

This paper cites Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior -Driven Development Acceptance Test Formulation.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior -Driven Development Acceptance Test Formulation

Reference 16

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Observation fff4ea20-5033-4170-bb70-d5dc016900d8 · outbound

This paper cites Cypress Copilot: Development of an AI Assistan t for Boosting Productivity and Transforming Web Application Testing.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Cypress Copilot: Development of an AI Assistan t for Boosting Productivity and Transforming Web Application Testing

Reference 17

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Observation aa5ccca8-95c5-4ef7-a6b9-8560901b7e36 · outbound

This paper cites A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images

Reference 18

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Observation b0ca3066-5d74-4870-a1ec-553b07cf8edc · outbound

This paper cites Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images

Reference 19

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Observation 9f5c50a8-1463-4bb7-b7a4-39afd84cdaf4 · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 20

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Observation f9b33564-3c1a-463b-81f0-b0eef27b0c18 · outbound

This paper cites Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

Reference 21

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Observation 2f6dab6b-8008-4a9a-9f6c-fcf5b5f9e429 · outbound

This paper cites Malaria parasite detection from peripheral blood smear images using deep belief networks.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Malaria parasite detection from peripheral blood smear images using deep belief networks

Reference 22

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Observation ca124a2d-ba16-4237-a4b0-3d0dab65b823 · outbound

This paper cites Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Reference 23

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Observation 63696c96-e5b4-4454-b95e-fd03e44091a4 · outbound

This paper cites Deep learning approach to detect malaria from microscopic images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Deep learning approach to detect malaria from microscopic images

Reference 24

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Observation cdb9e64e-9f65-4a4a-966c-157c54601189 · outbound

This paper cites Classification of malaria cell images with deep learning architectures.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Classification of malaria cell images with deep learning architectures

Reference 25

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Observation 28055260-5144-491e-aa31-d863e5a2e41b · outbound

This paper cites DeepFMD: computational analysis for malaria detection in blood - smear images using deep-learning features.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices DeepFMD: computational analysis for malaria detection in blood - smear images using deep-learning features

Reference 26

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Observation d56050e9-d137-44ae-ba7e-bd4db9b53d5e · outbound

This paper cites A dataset and benchmark for malaria life -cycle classification in thin blood smear images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A dataset and benchmark for malaria life -cycle classification in thin blood smear images

Reference 27

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Observation 2b5331ae-3c3b-44dc-b694-952b64b7bcbb · outbound

This paper cites DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection

Reference 28

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Observation 47e24059-932b-45de-affc-0e6c6818a8b4 · outbound

This paper cites A new ensemble learning approach to detect malaria from microscopic red blood c ell images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A new ensemble learning approach to detect malaria from microscopic red blood c ell images

Reference 29

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Observation 0d0b5031-d211-4d18-81e8-b63e159410f3 · outbound

This paper cites Deep learning for smartphone -based malaria parasite detection in thi ck blood smears.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Deep learning for smartphone -based malaria parasite detection in thi ck blood smears

Reference 30

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Observation 919b8c04-af30-4d6f-b039-bc7dacd937ae · outbound

This paper cites Visualizing deep learning activations for improved malaria cell classification.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Visualizing deep learning activations for improved malaria cell classification

Reference 31

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Observation c54d8152-1035-41d4-94e1-2929263ed08d · outbound

This paper cites Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network

Reference 32

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Observation 5907e953-9214-446a-bf01-21096190f790 · outbound

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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 33

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Observation 7b5413f3-3707-475e-a838-a81ccfa56bbf · outbound

This paper cites Detection of Malaria Parasite Using Lightweight CNN Architecture and Smart Android Application.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Detection of Malaria Parasite Using Lightweight CNN Architecture and Smart Android Application

Reference 34

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Observation 28907c4f-429f-4f86-a938-1dbcc2c3c7de · outbound

This paper cites A deep learning based framework for malaria diagnosis on high variation data set.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A deep learning based framework for malaria diagnosis on high variation data set

Reference 35

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Observation 5d4a2d75-4b8c-4e1f-935e-033082ff54fb · outbound

This paper cites Explainable AI Based Malaria Detection Using Lightweight CNN.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Explainable AI Based Malaria Detection Using Lightweight CNN

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.926827Z

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This paper cites Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.910817Z

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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 38

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Observation 82d092bf-4c70-4ad7-848a-e78dab5006b4 · outbound

This paper cites Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.880480Z

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Observation f0fb0a63-f368-477d-bfc1-1a3720ec8cef · outbound

This paper cites Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Im ages.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Im ages

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.865426Z

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Observation 1b6b920e-6e90-487e-aee9-fc63537859fa · outbound

This paper cites Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.850771Z

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Observation 596de1dd-64b0-49c0-8d04-f2ec4c95e154 · outbound

This paper cites Deep Machine Learning Model Trade-Offs for Malaria Elimination i n Resource-Constrained locations.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Deep Machine Learning Model Trade-Offs for Malaria Elimination i n Resource-Constrained locations

Reference 42

Resolution: verified exact; doi, observed 2026-08-10T15:22:31.546774Z

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Observation b5007518-7377-4a5a-89c6-ffe1a9c3af93 · outbound

This paper cites A Novel Shallow C onvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices A Novel Shallow C onvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images

Reference 43

Resolution: verified exact; doi, observed 2026-08-10T15:22:31.529967Z

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Observation eb296e32-86bf-48e7-9bae-29ed8820de45 · outbound

This paper cites Identification of mul tiple leaf diseases using improved SqueezeNet model.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Identification of mul tiple leaf diseases using improved SqueezeNet model

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.835471Z

Source-reported events for the cited work

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Observation a2e42740-bedf-4d5e-93e2-330588591e0d · outbound

This paper cites "COVIDiagnosis-Net: Deep Bayes- SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices "COVIDiagnosis-Net: Deep Bayes- SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-

Reference 45

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Observation 8534e997-71d5-4fb2-bf71-c13ded7a55eb · outbound

This paper cites An improved SqueezeNet model for the diagnosis of lung cancer in CT scans.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices An improved SqueezeNet model for the diagnosis of lung cancer in CT scans

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.788936Z

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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:22:31.804355Z

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Observation a999e299-3c2d-4ee6-a020-1cc3bf4f75cf · outbound

This paper cites Identification of tomato plant diseases by Leaf image using squeezenet model.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Identification of tomato plant diseases by Leaf image using squeezenet model

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.758212Z

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Observation bb2f06b7-6160-4950-9982-faa0f71596e9 · outbound

This paper cites Real -time vehicle make and model recognition with the residual SqueezeNet architecture.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Real -time vehicle make and model recognition with the residual SqueezeNet architecture

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.773684Z

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Observation b840f670-8c59-43da-8fc4-89b29f176210 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Imagenet classification with deep convolutional neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.727308Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9118db3e-4261-4adc-95bd-90956d9a43ae · outbound

This paper cites An electronic component recognition algorithm based on deep learning with a faster SqueezeNet.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices An electronic component recognition algorithm based on deep learning with a faster SqueezeNet

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.742659Z

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Observation bd23a3c6-a8c1-4837-9bf9-2cc4ae8924ba · outbound

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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:22:31.711742Z

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Observation 4effd424-ec6d-4da3-af22-f66e5176f0a7 · outbound

This paper cites an unresolved cited work.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:22:31.697306Z

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Observation f9bb8e6c-fde7-4bb8-aa8f-234f9d00827b · outbound

This paper cites With over two decades of expertise, he has established himself as a thought leader in artificial intelligence, deep learning, and machine learning solutions.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices With over two decades of expertise, he has established himself as a thought leader in artificial intelligence, deep learning, and machine learning solutions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:22:31.683088Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 84908f4b-8da6-4c77-b54a-502dcc14f271 · outbound

This paper cites an unresolved cited work.

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices Unresolved cited work

Reference 2284

Resolution: verified exact; doi, observed 2026-08-10T15:22:31.562873Z

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Pith citing papers

Observation 136bb989-b608-4095-9d1b-0dc2c7607bd8 · inbound

Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images cites this paper.

Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:40:56.452211Z

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