PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images

Adhikari, NCD and Guha, B and Alka, A and Das, U (2022) PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images. In: 5th Asia Conference on Machine Learning and Computing, ACMLC 2022, 28 - 30 December 2022, Hybrid, Bangkok, pp. 68-76.

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Abstract

Cancer is a group of diseases caused by abnormal cell growth, eventually leading to death. Cancer symptoms include chronic cough, breathing difficulties, weight loss, muscle stiffness, oedema, and bruises. Cancer detection increases with the stages, but unfortunately, the fatality also increases. In this research, we propose a pipeline coined as PiXelNet, which uses a classification system based on Convolutional Neural Networks (CNNs) that identifies three distinct kinds of lung cancer on histopathological images. The first step of the proposed network consists of a medical imaging analysis pipeline with models like ResNet, Efficient NetBO and MobileNet. We found that EfficientNet outperforms the other two models with a test accuracy of 99.33 and a loss of 0.0066. The second stage involves identifying the key areas from the original input test image with the feature extracted values. Using this strategy, the doctor or pathologist will immediately access all the crucial imaging heat maps and the network analysis report. © 2022 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2022 5th Asia Conference on Machine Learning and Computing, ACMLC 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Biological organs; Cell proliferation; Convolution; Deep learning; Diagnosis; Diseases; Medical imaging; Pipelines; Transfer learning, Convolutional neural network; Deep learning; Efficientnet; Histopathological images; Histopathology; Lung Cancer; Mobilenet; Resnet; Transfer learning; Weight loss, Convolutional neural networks
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	28 Nov 2023 10:15
Last Modified:	28 Nov 2023 10:15
URI:	https://eprints.iisc.ac.in/id/eprint/83276

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