| Abstrakt: |
Purpose: Breast cancer is a tumour affecting the breast tissues and is detected commonly in women. Computer-aided diagnosis aids in mass screening and early detection of breast cancer. Technological advancements in deep learning are driving the growth of automated breast cancer diagnosis. Various breast cancer image modalities like histopathology, mammograms, thermography, and ultrasound-based images are used for detecting breast cancer. This paper tends to explore a review of current studies related to breast cancer classification using deep learning techniques. Methods: The review highlights the imaging modalities, publicly available datasets, augmentation techniques, preprocessing techniques, transfer learning approaches, and deep learning techniques used by various researchers in the process of breast cancer early detection. In addition, the study also presents a comprehensive review of the performance of the existing deep learning algorithms, challenges, and future research directions. Results: The various methods proposed so far have been compared based on performance metrics including accuracy, sensitivity, specificity, AUC, and F-measure. Many research works have attained an accuracy of more than 90% in the classification and analysis of breast cancer detection using histopathological images. Conclusion: This review has pointed out various limitations that have to be improved for accurately identifying breast cancer using image modalities. Incorrect labelling of data due to observer variations with regard to image segmentation datasets, computation time, and Memory overhead has to be analyzed in future for resulting an enhanced CAD system. [ABSTRACT FROM AUTHOR] |
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