Muhammad Owais

TL;DR: A densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks is proposed.

TL;DR: A multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs and tested the generalization capability of the technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset.

TL;DR: A deep learning-based fully residual encoder–decoder network (FRED-Net) is proposed to determine the true iris region with the flow of high-frequency information from the preceding layers via residual skip connection and show the optimum performance of the proposed FRED- net on seven datasets of iris and general road scene segmentation.

TL;DR: The classification-based retrieval system of the multimodal medical images from various types of imaging modalities is proposed by using the technique of artificial intelligence, named as an enhanced residual network (ResNet), which demonstrates that the accuracy and F1.score by this method are higher than those by the previous method of CBMIR.

TL;DR: A dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers.