Medical Image Classification Using Deep Learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

Project Name	Stars	Most Recent Commit	Open Issues	License	Language
3dunetcnn	1,750	4 months ago	8	mit	Python
Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
U2net_torch	219	7 months ago	4		Python
MICCAI2019:3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Medical Image Classification Using Deep Learning	49	6 years ago	4		Python
Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
Ternarynet	7	6 years ago	2	mit	Python
see https://arxiv.org/abs/1801.09449
C Srcnn	6	5 years ago			Python
Context Aware Medical Image Super-Resolution Using Convolution Neural Networks
Papers	5	3 years ago	99	mit
Summaries of machine learning papers
Medical Image Segmentation A Survey	5	8 years ago
medical image segmentation: a survey