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This paper discusses the role of Transfer Learning (TL) and transformers incancer detection based on image analysis. With the enormous evolution of cancerpatients, the identification of cancer cells in a patient's body has emerged asa trend in the field of Artificial Intelligence (AI). This process involvesanalyzing medical images, such as Computed Tomography (CT) scans and MagneticResonance Imaging (MRIs), to identify abnormal growths that may help in cancerdetection. Many techniques and methods have been realized to improve thequality and performance of cancer classification and detection, such as TL,which allows the transfer of knowledge from one task to another with the sametask or domain. TL englobes many methods, particularly those used in imageanalysis, such as transformers and Convolutional Neural Network (CNN) modelstrained on the ImageNet dataset. This paper analyzes and criticizes each methodof TL based on image analysis and compares the results of each method, showingthat transformers have achieved the best results with an accuracy of 97.41% forcolon cancer detection and 94.71% for Histopathological Lung cancer. Futuredirections for cancer detection based on image analysis are also discussed.

Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection