Automating classification and segmentation process of abnormal regions in different body organs has a crucial role in most of medical imaging applications such as funduscopy, endoscopy, and dermoscopy. In recent years portable medical imaging devices such as capsule endoscopy and digital dermatoscope have been introduced and made the diagnosis procedure easier and more efficient. However, these portable devices have constrained power resources and limited computational capability. To address this problem, we propose a bifurcated structure for convolutional neural networks performing both classification and segmentation of multiple abnormalities simultaneously. It is trained first by each abnormality separately and then by using all abnormalities. In order to reduce the computational complexity, the network is redesigned to share some features which are common among all abnormalities. Later, these shared features are used in different branches to segment and classify the abnormal region of the image. Finally, results of the classification and segmentation are fused to obtain the classified segmentation map. The proposed framework is simulated using four frequent gastrointestinal abnormalities as well as three dermoscopic lesions. Properties of the bifurcated network such as low complexity and resource sharing make it suitable to be implemented as a part of portable medical imaging devices.
