A deep-XAI method for histopathological image classification: Utilizing transformer based FNet architecture and LIME method

The purpose of the study is to improve the cancer detection in medical images using the Fourier Net (FNet) architecture and the Local Interpretable Model-agnostic Explanations (LIME) method. The FNet architecture excels in extracting features from high-dimensional images and anatomical representations. LIME, on the other hand, is an algorithm to make the model\\\'s decisions interpretable. After applying the FNet architecture to the existing data, the LIME explainability method has been applied to determine whether the model outputs meaningful results from the image. Using deep learning techniques, the proposed algorithm represents cancer types with distinctive and robust features. An additional assessment by an expert pathologist was carried out to prove the results obtained after the LIME interpretation. Thus, medical professionals and researchers will be able to evaluate whether this method developed using FNet and LIME can provide a more interpretable and effective approach to cancer diagnosis. The proposed study lays the foundation for developing effective systems that assist doctors and pathologists in evaluating histopathological tissue images. Additionally, this study aims to enhance the reliability of machine learning methods.