In recent years, the intersection of medical imaging and deep learning has witnessed unprecedented advancements, revolutionizing the landscape of healthcare. One notable application that has gained substantial attention is medical image classification using Convolutional Neural Networks (CNNs). As we embark on this project, we delve into the realm of leveraging cutting-edge deep learning techniques to augment traditional medical image analysis.
Probelm statement: You are give data which contain 100's of x-rays. Your task is to build a effective and efficient CNN model to classify them into Normal and Infected category. Show you amazing DL skills to bulid best model :)
- For any concept/technique refer articles available on internet rather than using ChatGPT, as it may be misleading and many times provide only half information.
- Do not alter any pre-written code/comments.
- Write code in provided space only.
- Write commnets for what you did so that mentors can easy understand your work.
- Only use Google colab for running code.
- Fork and cloning this repository on your local device.
- Open each task on Google colab.
- Once task is completed download .ipynb file and store it in respective folder.
- Name your file as Enrollment no.
- For task1 store final file in Task1_solution with file name IIT2022119.
- Push this file to forked repo and then send PR.
- Your PR will be reviwed by the mentors. Once your PR is accepted, file will be merged and points will be granted.
For any query feel free to contact [email protected]. You can also interact with mentors and community on Discord