A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin

Using RRC Algorithm Classify the Proteins and Visualize in Biological Databases

Visualize biological database for protein is very complicated without Classify the protein properties.Protein classification is one of the major application of machine learning algorithms in the field of bio-informatics.The searching classification model works in two steps.Firstly, the correlation based feature selection for protein classification will be taken and strongly correlated features will be considered for classification using MST based . In second step, using Robust Regression, the classification will be performed. Based on results of RRC algorithm, it is highly has classification ratio than traditional machine learning algorithms such as SVM, Na�ve-bayes , Decision Trees

Using RRC Algorithm Classify the Proteins and Visualize in Biological Databases

Visualize biological database for protein is very complicated without Classify the protein properties.Protein classification is one of the major application of machine learning algorithms in the field of bio-informatics.The searching classification model works in two steps.Firstly, the correlation based feature selection for protein classification will be taken and strongly correlated features will be considered for classification using MST based . In second step, using Robust Regression, the classification will be performed. Based on results of RRC algorithm, it is highly has classification ratio than traditional machine learning algorithms such as SVM, Na�ve-bayes , Decision Trees