A novel end-to-end deep learning approach for cancer detection based on microscopic medical images.

TytułA novel end-to-end deep learning approach for cancer detection based on microscopic medical images.
Publication TypeJournal Article
Rok publikacji2022
AutorzyHammad M, Bakrey M, Bakhiet A, Tadeusiewicz R, EL-Latif AAAbd, Plawiak P
JournalBiocybernetics and Biomedical Engineering
Słowa kluczoweCAD, Cancer, CNN, Deep learning, Detection, End-to-end, Microscopic medical images

As a result of late diagnosis, cancer is the second leading cause of death in most countries in the world. Usually, many cases of cancer are diagnosed at an advanced stage, which reduces the chances of recovery from the disease due to the inability to provide appropriate treatment. The earlier cancer is detected, the more effective the treatment can be, especially for incurable cancers, which can result in a shorter life expectancy due to the rapid spread of the disease. The early detection of cancer also greatly reduces the financial consequences of it, as the cost of treating it in its early stages is much lower than in its other stages. Therefore, several previous studies focus on developing computer-aided cancer diagnosis systems (CACDs) that can detect cancer in its earliest stages automatically. In this paper, a novel approach is proposed for cancer detection. The proposed approach is an end-to-end deep learning approach, where the input images are fed directly to the deep model for final decision. In this research, the accuracy of a new deep convolutional neural network (CNN) for cancer detection is explored. The microscopic medical images obtained from the cancer database were used to evaluate our study, which were labelled as normal and abnormal images. The presented model achieved an accuracy of 99.99%, which is the highest accuracy compared with other deep learning models. Finally, the proposed approach would be very useful and effective, especially in low-income countries where referral systems for patients with suspected cancer are often unavailable, resulting in delayed and fragmented care.


Historia zmian

Data aktualizacji: 08/11/2022 - 14:49; autor zmian: Łukasz Zimny (lzimny@iitis.pl)