Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines

Balasundaram Ananthakrishnan (), Ayesha Shaik (), Shubhadip Chakrabarti, Vaishnavi Shukla, Dewanshi Paul and Muthu Subash Kavitha
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Balasundaram Ananthakrishnan: Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai 600127, India
Ayesha Shaik: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Shubhadip Chakrabarti: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Vaishnavi Shukla: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Dewanshi Paul: School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India
Muthu Subash Kavitha: School of Information and Data Sciences, Nagasaki University, Nagasaki 8528521, Japan

Sustainability, 2023, vol. 15, issue 2, 1-18

Abstract: Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the organs in the human body. It is found that histopathological images, obtained as a result of biopsy, are the most definitive way of diagnosing cancer. The main objective of this work is to use deep learning techniques for the detection and classification of adenocarcinoma using histopathological images of lung and colon tissues with minimal preprocessing. Two approaches have been utilized. The first method entails creating two CNN architectures: CNN with a Softmax classifier ( AdenoCanNet ) and CNN with an SVM classifier ( AdenoCanSVM ). The second approach corresponds to training some of the prominent existing architecture such as VGG16, VGG19, LeNet, and ResNet50. The study aims at understanding the performance of various architectures in diagnosing using histopathological images with cases taken separately and taken together, with a full dataset and a subset of the dataset. The LC25000 dataset used consists of 25,000 histopathological images, having both cancerous and normal images from both the lung and colon regions of the human body. The accuracy metric was taken as the defining parameter for determining and comparing the performance of various architectures undertaken during the study. A comparison between the several models used in the study is presented and discussed.

Keywords: cancer; adenocarcinoma; convolution neural network; CNN; transfer learning; CNN– SVM; medical image processing; deep learning; artificial intelligence; smart cancer diagnosis; AdenoCanNet; AdenoCanSVM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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