[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
最近更新: 6年多前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.
最近更新: 6年多前Analysis of breast cancer data published by the University of Wisconsin in order to predict benign vs malignant tumors based on tumor attributes. The dataset can be found at the following link: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
最近更新: 6年多前A web app for breast cancer detection (Under Development)
最近更新: 6年多前High-resolution breast cancer screening with multi-view deep convolutional neural networks
最近更新: 6年多前This is supplementary material for the manuscript: "Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks" M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou IEEE Journal of Biomedical and Health infomatics (2018)
最近更新: 6年多前
