A deep learning based approach for classification of CerbB2 tumor cells in breast cancer

Tataroglu, Gozde Ayse; Genç, Anıl; Kabakci, Kaan A.; Çapar, Abdulkerim; Töreyi̇n, Behçet Uğur; Ekenel, Hazım Kemal; Türkmen, İlknur; Çakır, Aslı

doi:10.1109/siu.2017.7960587

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…14 In recent days, breast cancer is the most commonly occurring type of cancer in women, and finds recurrent mention between women, and many research studies are concentrated more on this. 15 The death rate of breast cancer is rapidly increasing day by day, and for that the effective breast cancer classification model is very essential in the medical domain. Hence, early and precise identification of breast cancer is a tremendously significant process.…”

Section: Motivationmentioning

confidence: 99%

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

Salim

Sarath

2022

Int J Imaging Syst Tech

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One of the most predominant types of cancer affecting women is breast cancer. In computer‐assisted analysis of pathology, the most crucial task is classifying breast cancer automatically from the histopathological images. Most of the patients experienced everlasting effects and loss of life because of the absence of timely detection and thereby delay in treatment. Thus, the timely detection as well as precise identification of breast cancer using the histopathological image is highly required. An improved invasive weed water wave optimization (IIWWWO) algorithm is devised here, which is employed in training the Shepard convolutional neural networks (ShCNN) classifier. The developed optimization algorithm combines the improved invasive weed optimization algorithm and water wave optimization algorithm. Initially, the Bayesian filtering process is applied in the input image to remove the noises present in the input image. Moreover, the Hessian‐based Laplacian of Gaussian texture feature model is utilized in segmenting blood cells for an effective classification process. The shape features, namely shape area, diameter, and perimeter, are effectively extracted for better performance. Besides, ShCNN classifier is the most important one for classifying breast cancers. The proposed IIWWWO optimization technique is utilized in tuning the employed classifier termed as ShCNN classifier. Classification of breast cancer is accomplished using two levels, such as malignant and benign. The developed IIWWWO‐based ShCNN model is compared with various metrics, like accuracy, true negative rate (TNR) and true positive rate (TPR). The developed technique used to classify breast cancer obtained better accuracy of 95.83%, maximal TPR of 94.72%, and high TNR of 93.15%.

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Section: Motivationmentioning

confidence: 99%

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

Salim

Sarath

2022

Int J Imaging Syst Tech

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“…The mortality rate of breast cancer is higher when compared with other types [6]. Breast cancer is a general category of cancer that is recurrently found in women and various studies are being undertaken on breast cancer, especially in academia [7]. Breast cancer is generally diagnosed in women in around 140 to 184 countries worldwide [8].…”

Section: Introductionmentioning

confidence: 99%

An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

Peta

Koppu

2022

Electronics

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Internet of Things (IoT) plays an essential role in the area of the healthcare system. IoT devices provide information about patients in the healthcare monitoring framework. Moreover, patients can examine their health with smart devices and hence IoT is a major factor in all aspects of the health care management system. Breast cancer is a deadly cancer in women and the detection of this disease at the primary stage increases the survival rate. Due to the computational complexity associated with acquiring features, classification results generated from the existing methods are unsatisfactory and hence it is important to design a method using deep learning concepts for classifying cancer disease. An efficient and robust classification model named Student Psychology Whale Optimization-based Deep maxout network with optimization (SPWO-based Deep maxout network) classifies breast cancer disease. The advantage of using a Deep maxout network is that it effectively learns intrinsic features from the data. The weight factor of the deep learning model is updated with respect to iteration based on the fitness measure that in turn results in higher results by acquiring a minimal error value. However, the proposed model obtains outstanding accuracy, sensitivity, and specificity in terms of testing with the values of 0.931, 0.953, and 0.915 with 100 nodes.

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Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

et al. 2019

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A deep learning based approach for classification of CerbB2 tumor cells in breast cancer

Cited by 4 publications

References 9 publications

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

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