Neural Network architecture for breast cancer detection and classification

Jouni, Hassan; Issa, Mariam; Harb, Ahmad; Jacquemod, G.; Leduc, Yves

doi:10.1109/imcet.2016.7777423

Cited by 25 publications

(8 citation statements)

References 6 publications

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“…Borges and L. Rodrigues [5] have used J48 (DT) and Bayesian Networks on the same dataset after discretizing the data and proved that the Bayesian Networks gives the highest accuracy than J48. G. D. Rashmi et al [13] H. Jouni et al [9] proposed an architecture of optimal artificial neural networks that classifies breast cancer through pattern recognition. Authors focus was to reduce classification error by finding the optimal activation function with fewer blocks.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Israni¹

2019

IJITEE

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In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms

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Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Israni¹

2019

IJITEE

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“…g j is the activation function of the hidden layer and g k is the activation function of the output layer. A logsigmoid function was chosen as activation function for both hidden and output layers [18]. Due to the HCMOS9a technology and the power supply of the circuits (± 0.9 V [12]), we have chosen a dynamic of − 0.9 to 0.9 for the coefficients (i.e.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

“…Before running the design shown in Fig. 5, we must use the following variables as [18]: After executing the structure (see Fig. 5), we have the following results:…”

Section: Artificial Neural Network Signalsmentioning

confidence: 99%

“…malignant or benign) by using the artificial neural network MLP (Multi-Layer Perceptron) with back-propagation [14][15][16], based on the Wisconsin Data Base [17]. In a previous work, we have demonstrated that the most efficient architecture consists of 9 inputs neurons corresponding to 9 attributes of the Data Base, 10 neurons in the hidden layer and 2 for the output layer (two output classes: Benign or malignant), named 9-10-2 without biases [18]. The main objective of this work is to determine the influence of the nonidealities of the analog building blocks on the quality of the classification and to determine the specifications of each building block.…”

Section: Introductionmentioning

confidence: 99%

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Design and specification of analog artificial neural network

Jouni

Harb

Jacquemod³

et al. 2019

SN Appl. Sci.

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In this paper, we have implemented, using Matlab Simulink an analog artificial neural network for breast cancer classification. Simulated results with ideal building blocks exhibit a total error of classification of 2.6%. Thanks to this value, we have modified Simulink models of the building blocks (i.e. multiplier, activation function and its derivative) in order to take into account their non-idealities. This study allows to determine their influence on the classification quality and to extract some specifications of these building blocks.

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“…Typically cancer classification using the WBCD dataset is performed using digital AI algorithms implemented on power hungry GPUs [10][11][12][13]. Jouni [14] presented an optimal activation function for a CMOS based breast cancer tumor classifier implementation which minimizes classification error as well as area consumed by the classifier. The work Zhao [15] presented CMOS based x-ray imagers with low electronic noise for digital breast tomosynthesis (DBT).…”

Section: Introductionmentioning

confidence: 99%

A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

et al. 2020

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We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.

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Neural Network architecture for breast cancer detection and classification

Cited by 25 publications

References 6 publications

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Design and specification of analog artificial neural network

A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

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