IHDGAP: Deep Learning based Intelligent Human Diseases-Gene Association Prediction Technique for High Dimensional Human Diseases Data Sets

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

doi:10.36478/jeasci.2019.8072.8079

Cited by 2 publications

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“…Based on the WHO (World Health Organization), 450,000 patients die every year universal 3,4 . The mortality rate from breast cancer is detected using an effective screening process at initial stage of cancer 5 . The main measure for detection is to take an x‐ray of the breast region known mammogram 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Mammography is very operative for initial diagnosis as it is able to detect lesser change on tissues. This minor change can designate that presence of cancer 5,6 . The most recycled processes to diagnose breast cancer are mammography, biopsy, thermography and ultrasound 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…The most recycled processes to diagnose breast cancer are mammography, biopsy, thermography and ultrasound 7,8 . A biopsy is standard clinic methodology utilized to diagnose early‐stage cancer on microscope 9,10 . Based on subjective nature of human clarification, radiologists can have dissimilar opinions about related mammograms 7 .…”

Section: Introductionmentioning

confidence: 99%

“…This minor change can designate that presence of cancer. 5,6 The most recycled processes to diagnose breast cancer are mammography, biopsy, thermography and ultrasound. 7,8 A biopsy is standard clinic methodology utilized to diagnose early-stage cancer on microscope.…”

mentioning

confidence: 99%

“…7,8 A biopsy is standard clinic methodology utilized to diagnose early-stage cancer on microscope. 9,10 Based on subjective nature of human clarification, radiologists can have dissimilar opinions about related mammograms. 7 A false negative diagnosis phase can have serious penalties of patient.…”

mentioning

confidence: 99%

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A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

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In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

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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IHDGAP: Deep Learning based Intelligent Human Diseases-Gene Association Prediction Technique for High Dimensional Human Diseases Data Sets

Cited by 2 publications

References 0 publications

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

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

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