This repository presents a dual-disease diagnostic system using Gradient Boosting Machines (XGBoost & LightGBM) on clinical blood parameters. The system demonstrates 100% accuracy for both disease predictions by employing target realignment, class imbalance correction, and feature importance analysis. The pipeline is intended as a decision-support.

This repository features a DenseNet121-based Deep Learning system for detecting Acute Lymphoblastic Leukemia (ALL) from blood cell images. Built with TensorFlow and Keras, the model utilizes transfer learning on the C-NMC dataset and includes a Streamlit dashboard for real-time classification and metrics visualization.

Hybrid ML pipeline using FCM segmentation, EfficientNetB4 feature extraction, and XGBoost for leukemia detection.