Ml
Attention-Based Malaria & TB Screening
Five architectures compared properly, with significance testing
TensorFlow
Keras
CBAM
Grad-CAM
OpenCV
scikit-learn
Project Overview
A deep learning framework for automated screening of malaria in blood smears and tuberculosis in chest X-rays, built around Convolutional Block Attention Modules. The interesting part is not any single model but the comparison: five architectures evaluated under matched conditions, with statistical testing, interpretability, and deployment cost all measured rather than assumed.
Client
Research project
Role
Implementation (attention modules, pipeline, evaluation suite)
Completed
July 2026
Duration
Research implementation
Technologies Used
Frontend
Jupyter
Matplotlib
Seaborn
Backend
TensorFlow
Keras
scikit-learn
statsmodels
OpenCV
Deployment
Kaggle GPU
Google Colab
Other Tools
CBAM
Grad-CAM
ResNet50
VGG16
MobileNetV2
DenseNet121
