Proceedings ArticleDOI
Breast cancer diagnosis using Artificial Neural Network models
Rekh Ram Janghel,Anupam Shukla,Ritu Tiwari,Rahul Kala +3 more
- pp 89-94
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TLDR
A system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models is developed to assist the doctors in diagnosis of the disease.Abstract:
Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models. This will assist the doctors in diagnosis of the disease. We implement four models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization and Competitive Learning Network Experimental results show that Learning Vector Quantization shows the best performance in the testing data set This is followed in order by CL, MLP and RBFN The high accuracy of the LVQ against the other models indicates its better ability for solving the classificatory problem of Breast Cancer diagnosis.read more
Citations
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Journal ArticleDOI
State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.
Journal ArticleDOI
An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)
Meha Desai,Manan Shah +1 more
TL;DR: CNN is found to give slightly higher accuracy than MLP for diagnosis and detection of breast cancer.
Journal ArticleDOI
Probabilistic neural network for breast cancer classification
TL;DR: Three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer and PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively.
Proceedings ArticleDOI
Mathematical model development to detect breast cancer using multigene genetic programming
TL;DR: A 10 fold cross validated mathematical model to detect breast cancer using symbolic regression of multigene genetic programming (MGGP) produces fast and accurate results for both training and testing data set.
Journal ArticleDOI
Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images
TL;DR: Specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods.
References
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Journal ArticleDOI
A novel approach to microcalcification detection using fuzzy logic technique
TL;DR: The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzification image with the original image to preserve fidelity.
BookDOI
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
Patricia Melin,Oscar Castillo +1 more
Journal ArticleDOI
Breast Tumor Characterization Based on Ultrawideband Microwave Backscatter
TL;DR: It is suggested that both shape and size characteristics of a dielectric target can be classified directly from its UWB backscatter and therefore, characterization can easily be performed in conjunction with UWB radar-based breast cancer detection without requiring any special hardware or additional data collection.
Journal ArticleDOI
A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure
TL;DR: Complementary Learning Fuzzy Neural Network (CLFNN), FALCON-AART is proposed as the Computer-Assisted Intervention (CAI) tool for thermogram analysis, a neuroscience-inspired technique that provides intuitive fuzzy rules, human-like reasoning, and good classification performance.
Journal ArticleDOI
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications
Anna Karahaliou,I. Boniatis,Spyros Skiadopoulos,Filippos Sakellaropoulos,Nikolaos Arikidis,E. Likaki,G.S. Panayiotakis,Lena Costaridou +7 more
TL;DR: In this paper, the texture properties of the tissue surrounding micro calcification (MC) clusters on mammograms for breast cancer diagnosis were investigated using a probabilistic neural network, which achieved an area under receiver operating characteristic curve (Az) of 0.989.