Abstract
Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.
Highlights
Breast cancer is an unusual mass of the breast texture
Database Description. is method aims to quickly detect the breast tumors in MRI by using MATLAB R2016b software with a system configuration of 2.20 GHz CPU and 6.00 GB RAM. e main idea is to design an optimized convolutional neural network (CNN) to achieve promising results. It is implemented on the DCEMRI dataset, which is usually used for analyses of classification efficiency. e DCE-MRI dataset includes a set of 219 breast MR images that is collected from 105 different patients with breast cancer (55 tumor-like and 50 non-tumor-like malignant lesions), and 114 DCE-MRI were detected to be normal
A new comprehensive approach was proposed for the automatic detection of breast tumors. e method is a hybrid model, including an optimized design of a convolutional neural network and feature extraction-based technique to improve the classification efficiency
Summary
Input breast MRI image data should be simplified and prepared for the steps. us, in the first step, normalization is applied. Input breast MRI image data should be simplified and prepared for the steps. Noise reduction is the most important phase of preprocessing. MR images have problems such as electromagnetic (EM) noise emitted from circuits. E main cause of noise in MRI imaging can be of two types: (1) hardware and (2) subject (physiological noise, body motions, cardiac pulsation, respiratory motions, etc.). To overcome the noise problems of the breast MR images, they must be filtered. Noise removal is important in medical image processing. In this regard, an Intelligent Hybrid Filter is used. Is fuzzy-based filter is utilized to eliminate the noise of images. Is filter is used in particular for the preprocessing of medical images [8]. (1) e noisy image is passed in parallel from four noise removal filters (2) X is the input image, and X0, X1, X2, and X3 are the output of the filters (3) e output of the filters enters the fuzzy-neural system as input (4) Y is the output of the fuzzy-neural system and is the final improved image
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