Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection

TL;DR

This paper reviews the application of Transfer Learning and transformers in medical image analysis for cancer detection, highlighting transformers' superior accuracy in lung and colon cancer identification.

Contribution

It provides a comparative analysis of Transfer Learning methods, emphasizing transformers' effectiveness in improving cancer detection accuracy.

Findings

Transformers achieved 97.41% accuracy in colon cancer detection.

Transformers achieved 94.71% accuracy in lung cancer detection.

The paper discusses future directions for AI-based cancer detection.

Abstract

This paper discusses the role of Transfer Learning (TL) and transformers in cancer detection based on image analysis. With the enormous evolution of cancer patients, the identification of cancer cells in a patient's body has emerged as a trend in the field of Artificial Intelligence (AI). This process involves analyzing medical images, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRIs), to identify abnormal growths that may help in cancer detection. Many techniques and methods have been realized to improve the quality and performance of cancer classification and detection, such as TL, which allows the transfer of knowledge from one task to another with the same task or domain. TL englobes many methods, particularly those used in image analysis, such as transformers and Convolutional Neural Network (CNN) models trained on the ImageNet dataset. This paper analyzes and criticizes each method of TL based on image analysis and compares the results of each method, showing that transformers have achieved the best results with an accuracy of 97.41% for colon cancer detection and 94.71% for Histopathological Lung cancer. Future directions for cancer detection based on image analysis are also discussed.