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This project is focused on predicting machine failures in a manufacturing setting using classification models trained on sensor data. The dataset includes features such as tool wear, rotational speed, torque, and temperatures, and the goal is to classify the type of failure a machine is likely to experience.
University project within the Medical Imaging course: based on a selected image dataset (Skin Cancer MNIST: HAM10000), various methods of image processing and enhancement were used, including contrast adjustment, lesion segmentation, as well as the application of morphological operations, using the Python programming language
This deep learning project for breast cancer detection using PyTorch. It performs exploratory data analysis to understand patterns and distributions, followed by preprocessing and feature scaling. A neural network is then trained (with GPU support) to predict diagnoses from clinical data.