Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Malibari, Areej; Alshahrani, Reem; Al‐Wesabi, Fahd N.; Hassine, Siwar Ben Haj; Alkhonaini, Mimouna Abdullah; Hilal, Anwer Mustafa

doi:10.32604/cmc.2022.026131

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…In recent years, with the advancement of imaging computing techniques, diagnostic technologies in the medical field have gained a lot of interest. As a result, many researchers have attempted to enhance various body parts' segmentation and classification rate using different strategies as in [6][7][8]. This section presents a brief review of brain tumor diagnosis methods based on single modality and multimodality.…”

Section: Review Of Related Workmentioning

confidence: 99%

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

Rajeena¹,

Sivakumar²

2023

Intelligent Automation &Amp; Soft Computing

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An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person's health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images. The proposed approach starts with preprocessing the images to reduce the noise. Then, fusion rules are applied to get the fused image, and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region. Finally, a machine learning classifier classified the brain images into benign and malignant tumors. Computing statistical measures evaluate the classification potential of the proposed scheme. Experimental outcomes are provided, and the Enhanced Flower Pollination Algorithm (EFPA) system shows that it outperforms other brain tumor classification methods considered for comparison.

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Section: Review Of Related Workmentioning

confidence: 99%

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

Rajeena¹,

Sivakumar²

2023

Intelligent Automation &Amp; Soft Computing

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“…As for aided diagnosis for soft tissue sarcomas of the extremities, a fusion framework, constructed by AlexNet deep CNN and type-2 fuzzy sets, investigates the significance from MR images (Hermessi, Mourali et al 2019). MRI is also a widely used technique to classify and detect prostate cancer, and fuzzy k-nearest neighbor model is utilized for classification process (Malibari, Alshahrani et al 2022).…”

Section: Uncertain Medical Data 321 Imagesmentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. Medical data is usually uncertain because of the presence of noise, artifact or high dimensional unstructured information in the data. Fortunately, fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the problems above. Therefore, it is important and necessary to review the contributions of fuzzy deep learning for uncertain medical data in recent years. This paper first constructs four types of frameworks of fuzzy deep learning methods used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning methods, type of uncertain medical data and execution effects. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. We have found that although fuzzy deep learning indeed develops the interpretability, low-quality images, boundary point processing, imprecise text records processing and multi-source heterogenous data fusion in medical domain, challenges still exist and future research directions are provided from the perspective of genetic data processing, intelligent medical decision-making based on complex cognition information, health management of Traditional Chinese Medical and few-shot learning.

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“…A rising concentration of glutamate or free radical reactive species in hypoxic-ischemic encephalopathy, inflammatory cytokines such as TNF-α, IL-1b, IL-6, 12, 15, and 18 from activated microglia and astrocytes, low pH in infections, and free iron secondary to cerebral hemorrhage were broadly stated in both white- and grey-matter injuries as significant triggers for epileptic events [ 3 , 4 ]. An epileptic seizure is an ephemeral instance of complete or partial abnormal, unintended movement of the body that may be integrated with a loss of consciousness [ 5 , 6 ]. Even though epileptic seizures rarely occur in every person, their subsequent effect on social interaction, physical communication, and patient emotion makes the diagnosis and treatment of epileptic seizure of ultimate consequence [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

Hilal

Albraikan

Nour

et al. 2022

Biology

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Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.

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Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Cited by 10 publications

References 22 publications

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

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