Microscopy image analysis gives quantitative support for enhancing the characterizations of various diseases, including breast cancer, lung cancer, and brain tumors. As a result, it is crucial in computer-assisted diagnosis and prognosis. Understanding the biological principles underlying these dynamic image sequences often necessitates precise analysis and statistical quantification, a major discipline issue. Deep learning methods are increasingly used in bioimage processing as they grow rapidly. This research proposes novel biomedical microscopic image analysis techniques using deep learning architectures based on feature extraction and classification. Here, the input image has been taken as microscopic image, and it has been processed and analyzed for noise removal, edge smoothening, and normalization. The processed image has been extracted based on their features in microscopic image analysis using ConVol_NN architecture with AlexNet model. Then, the features have been classified using ensemble of Inception-ResNet and VGG-16 (EN_InResNet_VGG-16) architectures. The experimental results show various dataset analyses in terms of accuracy of 98%, precision of 90%, computational time of 79%, SNR of 89%, and MSE of 62%.

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The lack of awareness of blind spots in vehicle transport results in more deaths nowadays. To address this issue, the multi-obstacle detection and measurement of the depth of the nearing vehicle, height, and width is necessary. In recent years, Fuzzy logic is being used to access smart decision-making for control actions. To handle the specific task efficiently, ambiguous and imprecise linguistic data is required. In this context, a non-linear intelligent fuzzy decision-making system has been proposed to estimate blind spots. An inference engine, a defuzzification interface to identify the blind spot both day and night, and a fuzzy rule-base are included. Shadows and edges can be used as linguistic parameters to identify vehicles in the daytime. The lamps are elevated higher than the air dams to avoid casting a shadow under the car at night. One in-sourcing vehicle and three out-sourcing vehicles are tested to determine the driver’s blind spot and a more comfortable driver’s seat and a rear-view mirror using the proposed system. A fuzzy matrix with a triangular number obtained from the crisp matrix is used to alert the driver of the likelihood of a collision using LEDs or buzzers.

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