Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

Althubiti, Sara A.; Jos�Escorcia-Gutierrez,; Gamarra, Margarita; Soto-Diaz, Roosvel; Mansour, Romany F.; Alenezi, Fayadh

doi:10.32604/cmc.2022.028878

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…It employs the technique of artificial intelligence (AI) to perceptively comprehend the principle and physiological system of human illnesses through analyses in big data. It offers valuable data and direction for disease estimation, medical services, and clinical diagnosis [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Katib¹,

Ragab²

2023

Computer Systems Science and Engineering

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The concept of smart healthcare has seen a gradual increase with the expansion of information technology. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things (IoT), cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient. This work designs a new Heap Based Optimization with Deep Quantum Neural Network (HBO-DQNN) model for decision-making in smart healthcare applications. The presented HBO-DQNN model majorly focuses on identifying and classifying healthcare data. In the presented HBO-DQNN model, three stages of operations were performed. Data normalization is applied to pre-process the input data at the initial stage. Next, the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data. At last, the DQNN model is exploited for healthcare data classification. A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model. The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05% and 95.72% under the colon cancer and lymphoma dataset.

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Section: Introductionmentioning

confidence: 99%

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Katib¹,

Ragab²

2023

Computer Systems Science and Engineering

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show abstract

“…The superlative capability to mechanically abstract valuable features from the inherent features of data creates CNN the primary choice for complicated medical problems resolving [11][12][13]. Theretofore, CAD systems entrenched with DL methods function effectively for medical disease recognition by producing a variety of high-quality diagnostic resolutions by underlining suspicious characteristics in different fields of medical imaging [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Jos�Escorcia-Gutierrez¹,

Soto-Diaz²,

Madera³

et al. 2023

Computer Systems Science and Engineering

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Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

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Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment

Escorcia‐Gutierrez¹,

Gamarra²,

Leal³

et al. 2023

Computers and Electrical Engineering

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

Cited by 7 publications

References 18 publications

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment

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