Cancer Image Detection With PyTorch

## Project Request

This project involves using deep learning and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans. The dataset using for this project will be comprising of Positive Cell Adenocarcinoma Margin (PCAM) images.
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| Field | Description |
| ------ | --------------------------------- |
| About | Cancer Image Detection With PyTorch |
| Github | @Anurag9492722884 |
| Email | anuragpindiproli@gmail.com |
| Label | Project Request |

https://github.com/Anurag9492722884
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**Define You**

- GSSOC Participant
- Contributor

# Project Name

CanDetect

## Description

-This project uses deep learning in PyTorch and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans.

-The project leverages pre-trained Convolutional Neural Networks (CNNs) and transfer learning to improve the model's performance.

## Scope

-Early Cancer Detection: One of the primary objectives of cancer image detection is to identify potential cancerous regions at an early stage. By utilizing deep learning models built with PyTorch, the project can help analyze medical images, such as early signs of cancerous growths or anomalies.

-Accurate Diagnosis: Deep learning models trained on large datasets can learn complex patterns and features that may be difficult for human experts to detect. By leveraging PyTorch's capabilities, the project can develop models capable of accurately diagnosing cancer.

-Research and Development: The project can contribute to the ongoing research and development efforts in the field of cancer diagnosis and treatment.

## Timeline

Start time: Time of Assignment
End time: July 15th.