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This research enhances early disease diagnosis by analyzing retinal blood vessels in fundus images using deep learning. It employs eight pre-trained CNN models and Explainable AI techniques.
Deep convolutional neural networks for brain tumor detection leveraging transfer learning and optimization through fully convolutional neural networks.
Proyek ini adalah aplikasi web full-stack yang dirancang untuk deteksi otomatis penyakit pada daun padi. Pengguna dapat mengunggah gambar daun padi, dan sistem akan menganalisisnya menggunakan model machine learning yang telah dilatih sebelumnya.
This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.
A deep learning project for classifying seven core emotions from facial expressions using transfer learning on RGB images. Focused on model performance, efficiency, and real-world applicability.
This project evaluates and compares the prediction performances of various state of art pre-trained image classification models in classifying 5 types of flowers.
this project is based on brain tumor detection using image classification and deep learning models like CNN , KNN , Logistic Regression , XG-Boost , Random Forest and RESNET50V2. After testing these 6 models the best model among these 6 with high accuracy is taken and trained to the model and predicted the out put
This Dog Breed Classification model employs TensorFlow 2.0 and Transfer Learning with ResNet50v2 to accurately identify 70 different dog breeds from images, demonstrating the power of deep learning in image classification tasks.
This repository contains a fine-tuned Convolutional Neural Network Resnet50v2 model training for corneal eye ulcer classification. This training also shows the effect of data leakage on the separation of training data and validation data which can give overly optimistic results.
Project exploring the impact of node pruning on CNN performance metrics within a stratified k-fold cross-validation framework. Focuses on optimizing recall for medical imaging tasks while analyzing trade-offs in precision and accuracy