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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
Developed a fruit detection application using the YOLOv8 model, enabling users to identify and classify fruits in images through an intuitive PyQt5-based GUI.
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%
Classification of Tuberculosis (X-ray imagery).
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.
Code repository for training multi-label classification models on the CheXpert Chest X-ray dataset.
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.
'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.
Deep Learning Final Project
Helping my younger sister (9th grade of junior high school) crafting cloud classification and weather prediction model for a natural science assignment [ ON PROGRESS ]
🫘🫁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.
Deep Learning system for Diabetic Retinopathy detection using EfficientNetB3, DenseNet201 & ResNet50V2 with Explainable AI (Grad-CAM & Score-CAM). Achieves 96% accuracy
💡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 🏥.
🩺 Diagnose and refer tuberculosis cases swiftly with Tuberculosis.Ai, enhancing learning for pulmonologists through anonymized data submission.
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