Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Amine Bechar Youssef Elmir Rafik Medjoudj Yassine Himeur Abbes Amira
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.