01735nas a2200217   4500000000100000008004100001260001200042100001700054700002000071700002500091700001900116700001800135700002400153700002500177700002000202245013300222856007200355490000800427520106800435022001401503       2021                            d  c10/20211 aMoloud Abdar1 aMohammad Fahami1 aSatarupa Chakrabarti1 aAbbas Khosravi1 aPawel Plawiak1 aU. Rajendra Acharya1 aRyszard Tadeusiewicz1 aSaeid Nahavandi00aBARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification  uhttps://www.sciencedirect.com/science/article/pii/S00200255210071430 v5773 a<p>Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings.</p>
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