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The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

Skin cancer can be broadly classified into two major categories: Melanoma (Malignant) and non-melanoma (Benign). Melanoma is one of the deadliest kinds of cancer. However, the detection of this cancer at an early stage can help in improving the chances of survival.

This project aims to create a deep learning model for classifying fashion items using the Fashion MNIST dataset. Below, you can find the steps of the project and the results obtained.

Keras Convolutianl Networks

The primary objective s to develop an accurate and efficient classification model capable of identifying pneumonia cases in patients based on chest X-ray images. Pneumonia is a prevalent and potentially life-threatening respiratory infection. Early detection plays a critical role in timely intervention and effective treatment.

Utilized CNN models to classify images of mountains and forests, treating mountains as the positive class and forests as the negative class. We compare the performance of a pre-trained model, a custom CNN model, and a CNN model with data augmentation.