EMOPS: an enhanced multi-objective pswarm based  classifier for poorly understood cancer patterns

Subasree, S.; Gopalan, N. P.; Sakthivel, N.

doi:10.14419/ijet.v7i2.27.12102

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Figures [5][6][7][8][9][10][11][12][13] shows the simulation result of accuracy, sensitivity, specificity, precision, recall, F-score, ROC curve, skewness, kurtosis, mean, variance, SD, and ME obtained from the proposed BLIC-CRNN-MOSOA is likened to the existing BLIC-FrCN, BLIC-ICS-ELM, and BLIC-DCNN-BO methods, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, the deep learning has modernized the object recognition as well as image representation fields 12 . Unlike typical methods, deep learning delineates the convolutional neural network (CNN) structure that adapts to simulate human‐like information abstraction and retrieves lower medium and higher level features directly from raw imagery patches 13,14 …”

Section: Introductionmentioning

confidence: 99%

“…12 Unlike typical methods, deep learning delineates the convolutional neural network (CNN) structure that adapts to simulate human-like information abstraction and retrieves lower medium and higher level features directly from raw imagery patches. 13,14 The complex context of the classification procedure in classical deep learning models lessens the efficiency and accuracy of the system. The existing methods did not give adequate exactness, also error rate was raised.…”

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confidence: 99%

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Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Sakthivel¹,

Subasree²,

et al. 2022

Concurrency and Computation

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In recent years, a number of learning methods have been adopted for classifying the mammogram images, which helps the early detection and diagnosis of breast cancer. The breast lesion identification and categorization using mammography screening based on combined convolutional neural network and recursive neural network (CRNN) framework with parameters optimized using multi-objective seagull optimization algorithm (BLIC-CRNN-MOSOA) is proposed in this article. Initially, the unnecessary noise components are taken away from the mammogram images and the quality of the images are enhanced based on altered phase preserving dynamic range compression filtering approach. Then, the deep CRNN model with weight parameters optimized using multi-objective seagull optimization algorithm is adopted for classifying the mammogram images into three categories: (i) normal, (ii) benign, and (iii) malignant masses. The proposed BLIC-CRNN-MOSOA approach is executed in MAT-LAB platform, and its performance is compared with other deep learning classification approaches. Then the simulation performance of the proposed BLIC-CRNN-MOSOA method attains higher accuracy 99.67%, 98.38%, and 97.45%, higher sensitivity 98.33%, 89.34%, and 88.96%, higher specificity 93.15%, 91.25%, and 92.88% compared with existing methods, like BLIC-FrCN, BLIC-ICS-ELM, and BLIC-DCNN-BO. By this, the proposed method achieves higher classification accuracy with less misclassified error. Finally, the simulation results show that the proposed method is more efficient than the other classification methods. K E Y W O R D Saltered phase preserving dynamic range compression, breast cancer, convolutional neural networks, multi-objective seagull optimization algorithm, recursive neural networks INTRODUCTIONBreast cancer is one type of cancer that forms in the cells of the breasts. Women are mostly affected by this disease. In United States, new cases are reported as 0.25 million and mortality as 0.04 million. 1 Around one in eight women affected this disease in developed countries. A report states that the breast cancer is considerably raised globally. 2 A routine imaging procedure for screening is mammography. 3 It creates higher

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Sakthivel¹,

Subasree²,

et al. 2022

Concurrency and Computation

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Selection of Optimal Thresholds in Multi-Level Thresholding Using Multi-Objective Emperor Penguin Optimization for Precise Segmentation of Mammogram Images

Subasree

Sakthivel

Balasaraswathi

et al. 2022

J CIRCUIT SYST COMP

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In medical image examination, image segmentation is the broadly used method. Currently, the efficient segmentation of mammogram images is the main challenge. Many methods were presented for segmenting the mammogram images, but the results are not satisfactory. In this paper, an efficient segmentation of mammogram images-based Multilevel Thresholding (MLT) method is proposed. Initially, the preprocessing step is executed for eliminating the unnecessary noises. For gaining the useful features from the mammogram images, mammogram image segmentation is carried out using multilevel thresholding method. In this paper, a novel Multi-Objective Emperor Penguin Optimization (MOEPO) algorithm is proposed for searching the multilevel greatest thresholds that segment the images into background and objects. The objective functions of the MLT are Otsu’s method, Kapur and Tsallis entropy. The effectiveness of the proposed method is analyzed using several performances evaluating metrics, like PSNR, FSIM and SSIM. The experimental outcomes show that the performance of the proposed technique is superior to other state-of-the-art methods. The proposed technique is likened to three existing models, viz. ScPSO-MT, Double Threshold and IWO-SUSAN. The SSIM of the proposed technique is 24.99%, 27.83% and 26.95% better than ScPSO-MT, Double Threshold and IWO-SUSAN existing approaches. The PSNR of the proposed technique is 25.27%, 40% and 50.74% better than ScPSO-MT, Double Threshold and IWO-SUSAN approaches. The FSIM of the proposed technique is 28.57%, 34.12% and 34.12% better than ScPSO-MT, Double Threshold and IWO-SUSAN methods.

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Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

Subasree

Sakthivel

Shobana

et al. 2023

Int. J. Unc. Fuzz. Knowl. Based Syst.

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The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews’s correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.

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EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Cited by 4 publications

References 21 publications

Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Selection of Optimal Thresholds in Multi-Level Thresholding Using Multi-Objective Emperor Penguin Optimization for Precise Segmentation of Mammogram Images

Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

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