Cancer presents humans with an ongoing challenge in medical diagnosis and treatment. Histopathological analysis, the examination of tissue samples under a microscope, is a critical tool in detecting and diagnosing cancers, including lung and colon cancer. This repository aims to leverage advanced deep learning techniques to improve the accuracy and efficiency of histopathological image analysis.
- Dataset: Utilized LC25000 dataset consisting of high-quality microscopic images of Lung and Colon Cells.
- Model Architectures: Utilized and fine-tuned Xception and EfficientNetB5 for optimal results.
- Results: Achieved beyond human classification of cells with 99.7% generalized accuracy using Xception Net
- Pipeline: Designed a pipeline for future inference
Confusion Matrix
Classification Report
To get started with the project, follow these steps:
git clone https://github.com/Ahmaddimran/Histopathological-Lung-and-Colon-Cancer-Detection.git
Dataset -> https://academictorrents.com/details/7a638ed187a6180fd6e464b3666a6ea0499af4af
Any contribution is welcomed!
This project is licensed under the MIT License. See the LICENSE file for details
Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv:1912.12142v1 [eess.IV], 2019 https://arxiv.org/abs/1912.12142v1