Paper Citation Record · LEDGER

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.09804.

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pith.paper-citation-record.v1
2502.09804 v1

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Outbound references

Observation a0798066-59ca-487c-b337-35c26cb914ab · outbound

This paper cites Cancer today,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Cancer today,

Reference 1

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This paper cites Leukemia,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Leukemia,

Reference 2

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This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 3

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This paper cites Attention Is All You Need.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Attention Is All You Need

Reference 4

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This paper cites You only look once: Unified, real-time object detection,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models You only look once: Unified, real-time object detection,

Reference 5

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Observation a4a59f2c-c894-4f9e-b45d-b2ee654c3b8f · outbound

This paper cites A fast and efficient cnn model for b-all diagnosis and its subtypes classification using peripheral blood smear images,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A fast and efficient cnn model for b-all diagnosis and its subtypes classification using peripheral blood smear images,

Reference 6

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Observation fb718104-04c8-4d5d-8961-a80f600588df · outbound

This paper cites All challenge dataset of isbi 2019 (c-nmc 2019) (version 1),.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models All challenge dataset of isbi 2019 (c-nmc 2019) (version 1),

Reference 7

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Observation 05ae1652-3433-4bfc-b05b-d90e4dfe78e6 · outbound

This paper cites Yolov11 - key features,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Yolov11 - key features,

Reference 8

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Observation 66a718e4-ff11-4ed3-8cc9-98dedbbb3fc6 · outbound

This paper cites Yolov8: A novel object detection algorithm with enhanced performance and robustness,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Yolov8: A novel object detection algorithm with enhanced performance and robustness,

Reference 9

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Observation e674d67d-c36f-40f4-a896-a896e9189a53 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Deep Residual Learning for Image Recognition

Reference 10

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Observation deeeb9f8-4b99-4692-a832-856c9e0a35c4 · outbound

This paper cites Early diagnosis of acute lymphoblastic leukemia using yolov8 and yolov11 deep learning models,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Early diagnosis of acute lymphoblastic leukemia using yolov8 and yolov11 deep learning models,

Reference 11

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This paper cites DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection,

Reference 12

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Observation 7e38abeb-1ea0-42a1-94f8-90d93918da58 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 13

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This paper cites Imagenet classification with deep convolutional neural networks,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Imagenet classification with deep convolutional neural networks,

Reference 14

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This paper cites Going Deeper with Convolutions.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Going Deeper with Convolutions

Reference 15

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This paper cites A review of deep transfer learning and recent advancements,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A review of deep transfer learning and recent advancements,

Reference 16

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This paper cites Object detection in autonomous maritime vehicles: Comparison between yolo v8 and efficientdet,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Object detection in autonomous maritime vehicles: Comparison between yolo v8 and efficientdet,

Reference 17

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This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Inception-v4, inception-resnet and the impact of residual connections on learning,

Reference 18

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This paper cites A mobile application based on efficient lightweight cnn model for classification of b-all cancer from non-cancerous cells: A design and implementation study,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A mobile application based on efficient lightweight cnn model for classification of b-all cancer from non-cancerous cells: A design and implementation study,

Reference 19

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This paper cites A2m-leuk: attention-augmented algo- rithm for blood cancer detection in children,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A2m-leuk: attention-augmented algo- rithm for blood cancer detection in children,

Reference 20

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This paper cites Detection of acute myeloid leukemia using deep learning models based systems,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Detection of acute myeloid leukemia using deep learning models based systems,

Reference 21

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This paper cites Enhanc- ing acute lymphoblastic leukemia classification with a rapid and effective cnn model,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Enhanc- ing acute lymphoblastic leukemia classification with a rapid and effective cnn model,

Reference 22

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This paper cites A fuzzy based classifier for diagnosis of acute lymphoblastic leukemia using blood smear image processing,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A fuzzy based classifier for diagnosis of acute lymphoblastic leukemia using blood smear image processing,

Reference 23

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This paper cites A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods

Reference 24

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This paper cites Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks,

Reference 25

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This paper cites Vcaps-net: Fine- tuned vgg16 with capsule network for acute lymphoblastic leukemia detection on a diverse dataset,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Vcaps-net: Fine- tuned vgg16 with capsule network for acute lymphoblastic leukemia detection on a diverse dataset,

Reference 26

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This paper cites A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (svm-pso),.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (svm-pso),

Reference 27

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This paper cites Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by grad-cam visualization,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by grad-cam visualization,

Reference 28

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Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Imagenet large scale visual recognition challenge,

Reference 29

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This paper cites Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification,.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification,

Reference 30

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Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Caffe: Convolutional architecture for fast feature embedding,

Reference 31

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Observation 08349494-44fd-48ab-af0d-aa204748e271 · outbound

This paper cites Return of the Devil in the Details: Delving Deep into Convolutional Nets.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Return of the Devil in the Details: Delving Deep into Convolutional Nets

Reference 32

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This paper cites Available: https://proceedings.neurips.cc/paper files/ paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Available: https://proceedings.neurips.cc/paper files/ paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

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This paper cites Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Reference 2016

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This paper cites Available: https://docs.ultralytics.com/models/yolo11/ #key-features.

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models Available: https://docs.ultralytics.com/models/yolo11/ #key-features

Reference 2024

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