In this paper, four machine learning algorithms are compared to predict students’ future final exam results using past academic data from Samarkand State Univer In this paper, four machine learning algorithms are compared to predict students’ future final exam results using past academic data from Samarkand State Univer

One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it ha One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it ha

The article describes the parameters for evaluating the quality of images created based on pansharpening approaches. The measurements used to assess the image q The article describes the parameters for evaluating the quality of images created based on pansharpening approaches. The measurements used to assess the image q

Breast cancer is still a major global health concern, and early diagnosis requires precise and effective diagnostic technologies. This research provides a uniqu Breast cancer is still a major global health concern, and early diagnosis requires precise and effective diagnostic technologies. This research provides a uniqu

The U-Net model is an image segmentation technique primarily designed for image analysis in medicine. With minimal training data, this model is able to segment The U-Net model is an image segmentation technique primarily designed for image analysis in medicine. With minimal training data, this model is able to segment

Automated analysis for tumor segmentation and illness classification is hampered by the noise, low contrast, and ambiguity that are common in medical pictures. This work introduces a new 12-step fuzzy-based improvement pipeline that uses fuzzy entropy, fuzzy standard deviation, and histogram spread functions to enhance picture quality in CT, MRI, and X-ray modalities. The pipeline produces three improved versions per dataset, lowering BRISQUE scores from 28.8 to 21.7 (KiTS19), 30.3 to 23.4 (BraTS2020), and 26.8 to 22.1 (Chest X-ray). It is tested on KiTS19 (CT) for kidney tumor segmentation, BraTS2020 (MRI) for brain tumor segmentation, and Chest X-ray Pneumonia for classification. A Concatenated CNN (CCNN) uses the improved datasets to achieve a Dice coefficient of 99.60% (KiTS19, +2.40% over baseline), segmentation accuracy of 0.983 (KiTS19) and 0.981 (BraTS2020) versus 0.959 and 0.943 (CLAHE), and classification accuracy of 0.974 (Chest X-ray) versus 0.917 (CLAHE). A classic CNN is trained on original and CLAHE-filtered datasets. These outcomes demonstrate how well the pipeline works to improve image quality and increase segmentation/classification accuracy, offering a foundation for clinical diagnostics that is both scalable and interpretable.