Authors : Rupa Debnath; Rituparna Mondal; Arpita Chakraborty; Siddhartha Chatterjee

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/mv98uzw7

DOI : https://doi.org/10.38124/ijisrt/25may1339

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Cancer is a deadly disease with a minimal probability of curing when detected in the later stages. Out of many different varieties of cancer diseases, lung cancer falls under the critical category as it is internal, invisible, and connected to the breathing control mechanism of human beings. Lung cancer has become the most frequent type in this generation and has intensified in the post-COVID-19 pandemic era, with a high degree of fatality. The histopathological images of lung cancer are very large in volume, so that analyzing such kind of data is monotonous and error-prone in a human- controlled method. The recent progress in the field of computer vision, along with machine learning techniques, has made the path of research smooth in the medical and healthcare domain, also achieved the feasibility of data analysis with easy detection of cancer cells. The advent of the deep learning concept made the automatic and accurate detection of cancer cells from the histopathological image data analysis possible. In this paper, an evaluation and a methodological survey on Cancer cell detection and the accuracy benchmark of Cancer tissue Segmentation of whole slide images (WSI) using Machine Learning (ML) and Deep Learning (DL) approaches have been carried out. The critical analysis, along with the exploration of more probable research trends in the accurate interpretation of the cancer cell images, has also been addressed towards achieving the greater potential.

Keywords : Lung Cancer, Adenocarcinoma, Whole Slide Images, Convolution Neural Network, Generative Adversarial Network, Class Activation Mapping, Artificial Intelligence, Deep Learning, Image Processing.

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