scholarly journals Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiuzhen Cai ◽  
Xia Li ◽  
Navid Razmjooy ◽  
Noradin Ghadimi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Color Space ◽  
Breast Cancer Diagnosis ◽  
Discrete Wavelet ◽  
Human Errors ◽  
Thermal Exchange ◽  
Mammographic Images

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.

DIAGNOSIS OF BREAST CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 31 (05) ◽  
pp. 1950034 ◽  
Author(s):  
Homayoon Yektaei ◽  
Mohammad Manthouri ◽  
Faezeh Farivar
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Errors ◽  
Mammographic Images ◽  
Classical State ◽  
Malignant Breast ◽  
Classification Of Tumors ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

The diagnosis of benign and malignant breast cancer is a challenging issue today. Breast cancer is the most common cancer that women suffer from. The sooner the cancer is detected, the easier and more successful it is to treat it. The most common diagnostic method is the mammography of a simple radiographic picture of the chest. The use of image processing techniques and identifying patterns in the detection of breast cancer from mammographic images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect breast cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution as Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents mammography picture to several deep CNN with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state-of-the-art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches the diagnosis accuracy of [Formula: see text] using multiscale convolution technique which reveals the efficient proposed method.

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

scholarly journals Optimal Diagnosis of COVID-19 Based on Convolutional Neural Network and Red Fox Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ehsan Khorami ◽  
Fatemeh Mahdi Babaei ◽  
Aidin Azadeh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Red Fox ◽  
Region Of Interest ◽  
Disease Spread ◽  
Discrete Wavelet ◽  
Diagnostic Systems ◽  
Human Errors ◽  
X Ray

SARS-CoV-2 is a specific type of Coronavirus that was firstly reported in China in December 2019 and is the causative agent of coronavirus disease 2019 (COVID-19). In March 2020, this disease spread to different parts of the world causing a global pandemic. Although this disease is still increasing exponentially day by day, early diagnosis of this disease is very important to reduce the death rate and to reduce the prevalence of this pandemic. Since there are sometimes human errors by physicians in the diagnosis of this disease, using computer-aided diagnostic systems can be helpful to get more accurate results. In this paper, chest X-ray images have been examined using a new pipeline machine vision-based system to provide more accurate results. In the proposed method, after preprocessing the input X-ray images, the region of interest has been segmented. Then, a combined gray-level cooccurrence matrix (GLCM) and Discrete Wavelet Transform (DWT) features have been extracted from the processed images. Finally, an improved version of Convolutional Neural Network (CNN) based on the Red Fox Optimization algorithm is employed for the classification of the images based on the features. The proposed method is validated by performing to three datasets and its results are compared with some state-of-the-art methods. The final results show that the suggested method has proper efficiency toward the others for the diagnosis of COVID-19.

scholarly journals Image Processing & Neural Network Based Breast Cancer Detection

2019 ◽  
Vol 12 (2) ◽  
pp. 146
Author(s):  
Marwan Abo Zanona
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Cancer Patients ◽  
Human Perception ◽  
Discrete Wavelet ◽  
Breast Cancer Patients ◽  
Input Material ◽  
Processing Techniques ◽  
Mammogram Images

A large percentage of cancer patients are breast cancer patients. The main available methodology to examine the breast cancer is the Mammography. It detects the signs of breast cancer as different signs supports the experts’ decision. Actually, the Mammography is based on human perception and observations. So, build an AI computerized system will take major role in early signs detection. This paper presents an image processing with aid of artificial neural networks computations for computerized signs detection and exploration of breast cancer. The input material is the mammogram images, and the output helps the pathologists to take a decision. A set of input mammogram images was used for development, testing, and evaluation. The mammographic image will be preprocessed and then the features will be extracted using discrete wavelet transformation with aid of Weiner filtration. A historical data of extracted features were used to train a neural network, while the historical extracted features contains both Cancer and non-Cancer images. The combination of neural network machine learning, and rigid image processing techniques resulted accurate outputs. The methodology and results are showed and discussed later in this paper.

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