Optimization of Neural Network using Nelder Mead in Breast Cancer Classification

Kusuma, Edi Jaya; Shidik, Guruh Fajar; Pramunendar, Ricardus Anggi

doi:10.22266/ijies2020.1231.29

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…Our proposed ImbTree model achieves an average misclassification cost of 0.074, which is the least among all four algorithms. Experimental results of NM-BPNN proposed by Kusuma et al [20] prove that the traditional decision tree outperforms the neural network algorithm for unbalanced data of breast cancer. Table -5 show an accuracy comparison of the traditional decision tree [20], NM-BPNN [20], and ImbTree for the breast cancer dataset.…”

Section: Experimental Results and Analysismentioning

confidence: 96%

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ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

Barot¹,

Jethva²

2021

ijisae

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A reliable and precise tool for medical machine learning is in demand. The diagnosis datasets are mostly unbalanced. To propose an accurate prediction tool for medical data we need an accurate machine-learning algorithm for unbalanced data classification. In binary class unbalanced medical dataset, accurate prediction of the minority class is important. Traditional classifiers designed to improve accuracy by giving more weight to the majority class. Existing techniques gives good results by accurately classifying the majority class. Despite the fact that they misclassify the minority cases, the total accuracy value does not reflect this. When the misclassification cost of minority class is high, research should focus on reducing the total misclassification cost. This paper presents a new cost-sensitive classification algorithm that classifies unbalanced data accurately without compromising the accuracy of the minority class. Our proposed minority-sensitive decision tree algorithm employs new splitting criteria called MSplit to ensure accurate prediction of the minority class. The proposed splitting criteria MSplit derived from the exclusive causes of the minority class. For our experiment, we mainly focused on the breast cancer dataset by considering its importance in women's health. Our proposed model shows good results as compared to the recent studies of breast cancer detection. It shows 0.074 misclassification cost that is the least among the other comparison methods. Our model improves the performance for other unbalanced medical datasets as well.

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Section: Experimental Results and Analysismentioning

confidence: 96%

“…They use misclassification cost as evaluation parameters. Kusuma et al [20] proposed a back-propagation neural network (BPNN) based study for breast cancer detection. Authors use the Nelder Mead optimization technique to optimize the BPNN.…”

Section: Related Workmentioning

confidence: 99%

ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

Barot¹,

Jethva²

2021

ijisae

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“…3. BPNN structure with one hidden layer [20] process is then extracted to obtain the weight of each synapse. These weight sets were used as the initial state of the optimization model based on the simulated annealing (SA) algorithm.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Kusuma

Pantiawati

Handayani

2022

Knowl. Eng. Data Sci.

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Technology development in image processing and artificial intelligence field leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases that has the highest mortality cases around the world. Melanoma is one of the cancer types that appear caused by high exposure to UV light. The earliest the melanoma was identified, the higher the chance the patient can be recovered. Therefore, this study carries the melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data which contains 200 color digital images in BMP format. The data is processed using color feature extraction techniques to identify the characteristics of each image according to the target data. The color space extraction used includes mean RGB, HSV, CIE LAB, YCbCr, and XYZ. The evaluation result showed that the BPNN-SA method was able to increase the accuracy performance in classifying skin cancer when compared to the original BPNN method with an overall average accuracy of 84.03%.

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“…The performance was produced and observed with 92% of accuracy on multilayer NN model. Kusuma et al [14] proposed a Backpropagation NN optimization method using Nelder Mead for classifying the breast cancer appearance. Two different datasets were used namely, BCC dataset and Wisconsin Breast Cancer Dataset.…”

Section: Related Workmentioning

confidence: 99%

Optimized Deep Neural Networks Architecture Model for Breast Cancer Diagnosis

Kanimozhi¹,

Shanmugavadivu²

2021

YMER

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Breast cancer has increasingly claimed the lives of women. Oncologists use digital mammograms as a viable source to detect breast cancer and classify it into benign and malignant based on the severity. The performance of the traditional methods on breast cancer detection could not be improved beyond a certain point due to the limitations and scope of computing. Moreover, the constrained scope of image processing techniques in developing automated breast cancer detection systems has motivated the researchers to shift their focus towards Artificial Intelligence based models. The Neural Networks (NN) have exhibited greater scope for the development of automated medical image analysis systems with the highest degree of accuracy. As NN model enables the automated system to understand the feature of problem-solving without being explicitly programmed. The optimization for NN offers an additional payoff on accuracy, computational complexity, and time. As the scope and suitability of optimization methods are data-dependent, the choice of selection of an appropriate optimization method itself is emerging as a prominent domain of research. In this paper, Deep Neural Networks (DNN) with different optimizers and Learning rates were designed for the prediction of breast cancer and its classification. Comparative performance analysis of five distinct first-order gradient-based optimization techniques, namely, Adaptive Gradient (Adagrad), Root Mean Square Propagation (RMSProp), Adaptive Delta (Adadelta), Adaptive Moment Estimation (Adam), and Stochastic Gradient Descent (SGD), is carried out to make predictions on the classification of breast cancer masses. For this purpose, the Mammographic Mass dataset was chosen for experimentation. The parameters determined for experiments were chosen on the number of hidden layers and learning rate along with hyperparameter tuning. The impacts of those optimizers were tested on the NN with One Hidden Layer (NN1HL), DNN with Three Hidden Layers (DNN4HL), and DNN with Eight Hidden Layers (DNN8HL). The experimental results showed that DNN8HL-Adam (DNN8HL-AM) had produced the highest accuracy of 91% among its counterparts. This research endorsed that the incorporation of optimizers in DNN contributes to an increased accuracy and optimized architecture for automated system development using neural networks.

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Optimization of Neural Network using Nelder Mead in Breast Cancer Classification

Cited by 12 publications

References 18 publications

ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Optimized Deep Neural Networks Architecture Model for Breast Cancer Diagnosis

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