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Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data.

Random Spray Retinex (RSR) is an effective image enhancement algorithm due to its effectiveness in improving image quality. However, the algorithm computing complexity and the required hardware resources and memory accesses hampered its deployment in many applications scenarios, for instance in IoT systems with limited hardware resources. With the rise of Artificial Intelligence (AI), the use of image enhancement has become essential to improve the performance for many emerging applications. In this paper, we propose the use of the RSR as a pre-processing filter before the task of semantic segmentation of low-quality urban road scenes. Using the publicly available Cityscapes dataset, we compare the performance of a pre-trained deep semantic segmentation network on dark noisy images and on RSR pre-processed images. Our findings confirm the effectiveness of RSR in improving segmentation accuracy. In addition, to address the computation complexity and suitability to edge devices, we propose a novel efficient implementation of the RSR using resistive random access memory (RRAM) technology. The architecture provides highly parallel analog in-memory computing (IMC) capabilities. A detailed, efficient and low latency implementation of the RSR using RRAM-CMOS technology is described. The design is verified using SPICE simulations with measured data from fabricated RRAM and 65nm CMOS technologies. The approach provided here represents an important step towards low-complexity and realtime hardware-friendly architecture and design for Retinex algorithms for edge devices.

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