  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. Detecting multiple
abnormalities in each type of images is necessary for better and more accurate
diagnosis procedure and medical decisions. 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. The proposed network is
first trained by each abnormality separately. Then the network is trained 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 settings (directions) to
segment and classify the abnormal region of the image. Finally, results of the
classification and segmentation directions are fused to obtain the classified
segmentation map. Proposed framework is simulated using four frequent
gastrointestinal abnormalities as well as three dermoscopic lesions and for
evaluation of the proposed framework the results are compared with the
corresponding ground truth map. Properties of the bifurcated network like low
complexity and resource sharing make it suitable to be implemented as a part of
portable medical imaging devices.
