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Employing a fusion of UNet and ResNet architectures, the project endeavors to achieve multiclass semantic segmentation of sandstone images. Through deep learning techniques, it seeks to uncover microstructural features across various geological classifications.
People with pulmonary disease often have a high opacity, which makes segmentation of the lung from chest X-rays more difficult. In this study, I propose a methodology to improve the performance of the U-NET structure so that it is able to extract the features and spatial characteristics of the X-ray images of the chest region.
A tiny version of original U-Net architecture was used to detect vehicles with segmentation vehicle classes: car, trailer, bus, rider, motorcycle, truck.
Dermatologists suffer from the difficulty of locating cancerous and malignant skin lesions, which causes many problems during the process of removing the tumor, which leads to the return of the tumor again. In determining the location of the tumor and its spread and determining the area that must be removed accurately.