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A Proposed Artificial Neural Network Classifier to Identify Tumor Metastases

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M. Khoshnevisan, Sukanto Bhattacharya, Florentin Smarandache

General Mathematics · Submitted 9 Dec 2002

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases.

how it is stated · asserted 0.93 read the record →

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arXiv IDmath/0212122 (version 1)
Pith IDZQEXXJQJ, Pith's permanent record of this paper and its review
CitationFlorentin Smarandache, M. Khoshnevisan, Sukanto Bhattacharya. "A Proposed Artificial Neural Network Classifier to Identify Tumor Metastases." arXiv:math/0212122 (2002). https://pith.science/paper/ZQEXXJQJ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,301 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.28 · human participants 0.50 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.04 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. contribution 0.99 0.61 0.26 asserted 0.93
s1 A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables. method 0.99 0.51 0.04 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.68.

What was examined, and what was not

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not examined
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  • figures and tables
  • references and citing sentences
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  • P abstract
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  • T body sentences
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables.

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