Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network

Saini, Satish; Vijay, Ritu

doi:10.1109/csnt.2015.78

Cited by 20 publications

(9 citation statements)

References 7 publications

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“…The cascade forward back-propagation model uses the postpropagation algorithm to update weights such as back-propagation neural network; however, the main characteristic of this network is that each layer of neurons is linked to all previous neuron layers. 24 …”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review

Sadoughi¹,

Kazemy²,

Hamedan³

et al. 2018

BCTT

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Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide; thus, its early diagnosis has a significant impact on reducing mortality. However, it is often difficult to diagnose breast abnormalities. Different tools such as mammography, ultrasound, and thermography have been developed to screen breast cancer. In this way, the computer helps radiologists identify chest abnormalities more efficiently using image processing and artificial intelligence (AI) tools. This article examined various methods of AI using image processing to diagnose breast cancer. It was a review study through library and Internet searches. By searching the databases such as Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Springer, IEEE, ScienceDirect, and Gray Literature (including Google Scholar, articles published in conferences, government technical reports, and other materials not controlled by scientific publishers) and searching for breast cancer keywords, AI and medical image processing techniques were extracted. The results were provided in tables to demonstrate different techniques and their results over recent years. In this study, 18,651 articles were extracted from 2007 to 2017. Among them, those that used similar techniques and reported similar results were excluded and 40 articles were finally examined. Since each of the articles used image processing, a list of features related to the image used in each article was also provided. The results showed that support vector machines had the highest accuracy percentage for different types of images (ultrasound =95.85%, mammography =93.069%, thermography =100%). Computerized diagnosis of breast cancer has greatly contributed to the development of medicine, is constantly being used by radiologists, and is clear in ethical and medical fields with regard to its effects. Computer-assisted methods increase diagnosis accuracy by reducing false positives.

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Section: Resultsmentioning

confidence: 99%

“…During the training, computation is performed from the input layer to the output layer and error values are released to the previous layer. 24 …”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review

Sadoughi¹,

Kazemy²,

Hamedan³

et al. 2018

BCTT

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show abstract

“…The obtained accuracy was 98.52 % using the dataset of fine-needle aspiration. In [5], a technique of breast cancer detection using the models of image processing was proposed based on Artificial Neural Networks (ANN). Gray Level Co-Occurrence Matrix (GLCM) feature extraction is utilized for training ANN.…”

Section: Breast Cancer Is One Of the Most Common Kinds Of Cancers Thamentioning

confidence: 99%

Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Hasan

Mohammed

Waleed

2020

EEJET

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“…Mean Square Error assess the performance and compare the accuracy of both the structure. [7]. This paper proposes the method for diagnosis of breast cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

Nagtode¹,

Potdukhe²

2015

IJIPHM

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Human system is made up of many organs, brain is the first and the leading controller of the human system. Abnormal growth of the Overload cells in brain is called as brain tumor. In medical field brain tumor detection and classification is one of the difficult tasks. Early detection and classification of brain tumor obviously increases the chances of proper treatment. In this review paper specially focus on detection and classification of the brain tumor from MRI images of brain. Nowadays Medical Image Processing is a complex and challenging field. This review paper proposed the techniques this techniques is very useful to give the information about brain tumor which is very helpful to the doctor for giving the proper treatment.. This review paper proposed the 2D Gabor wavelet transform technique for detection of brain tumor. Proposed technique perform the systematic evaluation of the database of MRI brain images. Artificial neural network use for the Automated and accurate classification of MRI images of brain tumor. Artificial neural network Classify the Brain Tumor into three types that is benign, malignant or normal. Probabilistic Neural Network is the part of the artificial neural network. The performance of the Probabilistic Neural Network classifier evaluation is based on training set and accuracy of classification. Probabilistic Neural Network gives proper result of classification than other artificial neural networks and it is a optimistic tool for classification of the brain Tumors .

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Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network

Cited by 20 publications

References 7 publications

Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review

Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review

Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

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