This project uses mammograms for breast cancer detection using deep learning techniques.

A food image to recipe converter for Indian food.

Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)

Transfer-Learning based Sugarcane Leaf Disease Detection Using DenseNet201 Architecture

Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays

Classification of Tuberculosis (X-ray imagery).

We have proposed a multimodal approach. Where we first took the best unimodal for textual and visual data classification by testing and automation process. Then we fusion of the two models which can successfully classify the materials that have been damaged using the image and text data. EfficientNetB3+BERT multimodal better accuracy with 94.18%

This project uses mammograms for breast cancer detection using deep learning techniques.

A web app to predict whether a person has COVID-19 from their Chest X-Ray (CXR) scan by image classification using Transfer Learning with the pre-trained models VGG-16 and DenseNet201 with ImageNet weights.

Developed a fruit detection application using the YOLOv8 model, enabling users to identify and classify fruits in images through an intuitive PyQt5-based GUI.

Thesis Project

Code repository for training multi-label classification models on the CheXpert Chest X-ray dataset.

Makerere Passion Fruit Disease Detection Challenge

CNN to develop a system that can automatically classify these X-rays as either indicative of COVID-19 infection or negative for the virus.

Deep Learning Final Project

'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.

Helping my younger sister (9th grade of junior high school) crafting cloud classification and weather prediction model for a natural science assignment [ ON PROGRESS ]

💡Utilizing deep learning techniques 🧠 and models such as ResNet50, VGG16, ResNet101, VGG19, DenseNet201, EfficientNetB4, and MobileNetV2 🤖 through transfer learning and fine-tuning 🔧 to improve lung cancer detection from CT scans 🏥.

🫘🫁NephroScan is an AI-powered kidney stone detection system that uses deep learning models ResNet50, MobileNetV3, and DenseNet201 to analyze kidneys for the presence of stones. The backend server processes medical images and returns detection results via a REST API, seamlessly integrated into a mobile application for real-time diagnostics.

A deep learning project for food image classification using the Food-101 dataset, leveraging DenseNet201 architecture. Includes nutritional facts estimation with scope for improvement in accuracy and scalability.