noamko128 / histopathology-images-classification

Histopathology Images Classification

Project Overview: This project revolves around histopathology image classification using various deep learning models. The objective is to develop a robust classification system capable of accurately categorizing histopathological images of colorectal tissue into different classes.

Datasets:

The project relies on the Colorectal Histology dataset available through TensorFlow Datasets (TFDS) at dataset link. This dataset contains a diverse collection of histopathological images representing different tissue types and conditions, making it a valuable resource for training and testing deep learning models.

Key Components:

Deep Learning Models: Several deep learning architectures are explored for image classification, including Convolutional Neural Networks (CNNs), Transfer Learning models, and potentially custom architectures tailored to the specific task.

Project Goals:

The primary goals of this project include achieving high accuracy in classifying histopathology images and assessing the performance of different deep learning models in this medical image analysis task. The project may also involve fine-tuning and optimizing model hyperparameters for improved results.

Significance:

Histopathology image classification is of immense significance in medical diagnosis and pathology. Accurate classification of tissue samples can aid in early disease detection and contribute to improved patient care.

*This project is my final project in the Deep Learning course as part of my Bachelor's degree in Digital Medical Technologies.