Automatic microscopic diagnosis of diseases using an improved UNet++ architecture

Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system based on microscopic image processing of patient tissue samples for the automatic diagnosis of anthrax and other tissues diseases, metastasis detection, patient prognosis, etc. An improved UNet++ architecture is proposed to segment microscopic images of patient tissue samples. The proposed model combines multi-scale features by adding skip connections in two paths; the forward path from the encoder to the decoder and the decoder path to the output. These new connections improve the performance of the UNet++. Integration of the squeeze and excitation-inception blocks in the new skip connections provides the network with features at different scales with different kernel sizes. Several convolutional networks are used as the backbone to extract powerful representations in the encoder section. The use of batch normalization, dropout technique, and LRelu activation function in this model accelerates convergence and increases the generalization power of the model. To overcome the problem of data imbalance of different classes, a weighted hybrid loss function is proposed, which further improved segmentation efficiency. The semantic segmentation results are converted to the instance segmentation using the marker-based watershed algorithm. Experimental results show that despite many challenges of microscopic image analysis, the proposed model is a reliable system for the automatic diagnosis of anthrax and other tissues diseases. It produces better results than state-of-the-art architectures.