Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.

Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.

Accurate segmentation of breast cancer (BC) in ultrasound images is a complicated task due to the variable nature of ultrasound images. Recently, many techniques are suggested to accurately segment BC using ultrasound imaging and deep learning. A multiscale cascaded convolution with residual attention-based double decoder network for BC segmentation is presented in this study. A multiscale cascaded convolution operation-based encoder path is designed to overcome the problem of a single scale feature learning process. The proposed multiscale convolution operation helps to extract the diverse semantic spatial features. In the segmentation process, a residual attention-based double decoder network is proposed. The proposed attention mechanism is implemented to take out the more prominent features from the tumor region and to suppress the other information that can mislead the segmentation model during training. The double decoding mechanisms are introduced to capture the highly diverse spatial features that are learned in the encoder path. For experimental purposes two publically available ultrasound image datasets namely BUSI and UDIAT are utilized. The proposed U-shaped multiscale cascaded convolution with residual attention-based double decoder network achieved the segmentation dice of 91.38% with 81.67% of the Jaccard index, 94.43% precision, and 87.76% recall score on the UDIAT dataset. A dice score of 90.55% with a Jaccard index of 80.87%, 93.53 % precision score, and 88.46% recall score are recorded on the BUSI dataset. The results of the proposed multiscale cascaded convolution with residual attention-based double decoder network validated that it can effectively be used for breast cancer segmentation tasks.