k0ooz's starred repositories
COVID-19-CT-Diagnose
Pretrained (imagenet) ResNet50 with 3 classification. Achieves 97.2% test accuracy. Use Grad-CAM for infected area segmentation.
fine_tune_resnet50_keras
fine tune resnet50 keras pretrained model.
cifar100-resnet50-pytorch
ResNet50 pretrained transfer learning for CIFAR100 in Pytorch
ImageClassificationProject-IITK
This repository contains the Jupyter notebook for the custom-built VGG16 Model build for the Tiny ImageNet dataset.
Chest-Xrays
A CNN model with transfer learning using a VGG16 model trained ImageNet dataset.
Image_Processing_And_Transfer_Learning_For_Dectection_Of_Leukemia
Blood cancer is an uprising issue and doing physical medical procedures is too sensitive and time-consuming to detect any blast cell. Manual testing includes blood tests, spinal fluid tests, bone marrow tests, imaging tests, etc. A solution to this is to use modern methods in health care that help to detect diseases faster and increase the cure rate.ssing and Transfer Learning for Detection of Types of Leukemia: In image processing, data preparation and image preprocessing are done where we have rescaled the image and adjusted the brightness to improve the image quality. Data augmentation is performed to increase the image count by flipping it horizontally and vertically. Images are converted to grayscale to reduce the matrix calculation.The images in the dataset are: AML has 935 images, ALL has 858, CML has 623 and CLL has 510. Transfer learning is used. I have used different pre-trained CNN models such as ResNet-50, VGG16, Inception V3, and MobileNet for feature extraction and classification.VGG16, InceptionV3 and MobileNet - all three models give 100% accuracy, while ResNet50 gives 85% accuracy.
Malaria_Classification_usingDL
In this project, I implemented algorithms (VGG16, VGG19, and CNN) to develop a malaria detection system using blood cell images. The goal was to automate the traditional method of identifying malaria, which involves examining blood smears under a microscope.
msc_final_year_project
Blood cell type prediction with ResNet50 in python, tensorflow and keras
BloodCell-Detection-Datatset
This is a dataset of blood cells photos.
Chula-RBC-12-Dataset
Dataset for Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset
CV_Dogs_or_Cats_TF
About This repository contains a binary classification project for discriminating between dog and cat images using the EfficientNet model. It contains two Jupyter notebooks: one for training the model and another for evaluating and testing the model on unseen images.
ImageClassification
Image Classification of Cat and Dog Images with a self made CNN vs. Googles EfficientNet.
resnet50-transfer-learning-cats-and-dogs
Using TensorFlow API to transfer a ResNet-50 neural network and connect new fully connected layers to classify cats and dogs images.
SE20UARI146_Assignment3_Q2
Fine-Tune ResNet model to classify cat and dog.
Image-Classification-Using-VGG16-Pretrained-Model
VGG16 pretrained model for image classification (cat, dog, and bird)