Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Balaji, Prasanalakshmi; Chidambaram, Kumarappan

doi:10.3390/diagnostics12010011

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…The most common type of melanoma is percutaneous melanoma, which forms on the skin. In certain circumstances, melanoma can develop from a mole, allowing for effective treatment if detected early [ 1 , 2 ]. Melanoma is a fatal type of skin cancer that speedily spread on the body that appeared through the malignant shift of melanocytes which is imitated in distinction to neural crest neoplasia.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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“…However, deep neural networks, for example, CNN and RNN, are often employed yet have not even been demonstrated to be useful. Improvements have been made to the defect prediction mechanism [2,3]. e creation of software flaws is caused by developers' incorrect understanding of the design of software requirements, or negligence when developing software, and problems with internal calls of system.…”

Section: Introductionmentioning

confidence: 99%

Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network

Liu

Sanober

Zamani

et al. 2022

Security and Communication Networks

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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.

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“…With the optimization factor, the network reached an accuracy of 84.41%. Balaji et al [21] proposed a social spider optimization (SSO) algorithm to improve CNN output to achieve the pathological classification of breast cancer biopsy images. The SSO algorithm adjusts the CNN network's weights, achieving an accuracy of 95.91% and a sensitivity of 94.25%.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

et al. 2023

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Breast cancer is the second most common type of cancer among women, and it can threaten women’s lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient’s abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.

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Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Cited by 9 publications

References 29 publications

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

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