Fully automated model on breast cancer classification using deep learning classifiers

Ghrabat, Mudhafar Jalil Jassim; Hussien, Zaid Alaa; Khalefa, Mustafa S.; Abduljabbar, Zaid Ameen; Nyangaresi, Vincent Omollo; Sibahee, Mustafa A. Al; Abood, Enas Wahab

doi:10.11591/ijeecs.v28.i1.pp183-191

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…Afterward, Fourier and circularity descriptors can be extracted in the effectual method corresponding to the texture and affine shape adaptation features. In [35], a fully automated method has been designed utilizing the DL approach to capture images in patients and pre-processing, segmentation, and classifying the intensity of cancer spread.…”

Section: Related Workmentioning

confidence: 99%

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Issaoui,

Alohali,

Mtouaa

et al. 2024

IEEE Access

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Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an Archimedes Optimization Algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models. INDEX TERMSContent-based image retrieval; Healthcare; Similarity measurement; Deep learning; Archimedes optimization algorithm I. INTRODUCTIONRecently, the accessibility and application of numerous medical imaging approaches namely positron emission tomography (PET), magnetic resonance imaging (MRI), and X-ray scans have led to a dramatic expansion in the size of medical image sources [1]. It highlights the requirement for emerging beneficial healthcare information management systems (HIMS), which are assisted in easy-to-use and instinctive applications like query-based image retrieval, similar image retrieval, and image classification to allow This article has been accepted for publication in IEEE Access.

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Section: Related Workmentioning

confidence: 99%

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Issaoui,

Alohali,

Mtouaa

et al. 2024

IEEE Access

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“…In this regard, vulnerability prediction models play critical roles in the identification of the location of source code that require more attention [41], [90]. For instance, authors in [91] have used machine learning [92] techniques to predict the vulnerability of the software from source code before its release. Similarly, a security assurance framework is developed in [93] for connected vehicular technology.…”

Section: Security Models and Frameworkmentioning

confidence: 99%

Software security models and frameworks: an overview and current trends

Korir¹

2023

World J. Adv. Eng. Technol. Sci.

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The continued dependence on information technology applications has led to the adoption of electronic channels and software applications to support businesses, online transactions and communications. In this perspective, both functional and non-functional requirements are critical for the provision of necessary needs at the early phases of the software development process. This is specifically important in the requirement phase of the software development process. Software security is increasingly becoming a necessity concern in this environment. Unfortunately, security concepts such as access control requirements are mostly considered after the functional requirement definition stage. In addition, majority of the developers and organizations regard security as an activity that can be incorporated after the development of a system. This leads to flaws and security defects in the access control mechanism. Therefore, software security issues must be given higher priority in the early stages of the development process. The aim of this paper is to offer a survey of the software security techniques, frameworks and models that have been developed to deal with these issues. The results obtained indicate that software vulnerabilities have made it possible for attackers to exploit them to cause havoc in computerized systems and has become one of the most critical risks facing modern corporate networks. Since most of these vulnerabilities involve exploitable weaknesses introduced through badly written code, the cyber security community has tried to come up with techniques to enhance software products.

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“…After rigorous testing, the patch is applied to the software, which is the followed by the release of the patch [271]. To address the software system security, various models [272], practices, strategies, and methods have been proposed. They have been shown to improve security procedures in the stages of SDLC [273].…”

Section: Approaches To Software Quality and Securitymentioning

confidence: 99%

An investigation into software quality and security: Past research works and existing gaps

Korir¹

2023

Global J. Eng. Technol. Adv.

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Software security is concerned with the protection of data, facilities and applications from harm that may be occasioned by malware attacks such as password sniffing, viruses and hijacking. It is a system-wide concept that takes into account both security mechanisms such as access control as well as the design for security, such as a robust design that renders software attack complicated. It may encompass building of secure software, which comprises of the designing of software to be attack-resistant, ensuring that software is error-free, and educating software developers, architects, and users about the building of secure artifacts. In this regard, insecure software negatively affects organization’s reputations with customers, partners, and investors. The goal of this paper is to investigate some of the issues that make the software insecure, as well as the approaches that have been developed to boost software quality and security. The outcomes indicate that various models, techniques, frameworks and approaches to software quality have been developed over the recent past. However, only a few of them give reliable evidence for creating secure software applications.

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Fully automated model on breast cancer classification using deep learning classifiers

Cited by 23 publications

References 6 publications

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Software security models and frameworks: an overview and current trends

An investigation into software quality and security: Past research works and existing gaps

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