Brain tumor diagnosis based on metaheuristics and deep learning

A Hu, N Razmjooy - International Journal of Imaging Systems …, 2021 - Wiley Online Library
The high mortality rate associated with brain tumors requires early detection in the early
stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis …

Enhanced region growing for brain tumor MR image segmentation

ES Biratu, F Schwenker, TG Debelee, SR Kebede… - Journal of …, 2021 - mdpi.com
A brain tumor is one of the foremost reasons for the rise in mortality among children and
adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces …

New brain tumor classification method based on an improved version of whale optimization algorithm

B Yin, C Wang, F Abza - Biomedical Signal Processing and Control, 2020 - Elsevier
Brain tumor is an abnormal growth of cells in the brain that its diagnosis in the early stages
can help us to prevent the dangers of the next stage. In this paper, a new meta-heuristic …

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT‐Enabled MRI Devices

D Kaur, S Singh, W Mansoor, Y Kumar… - Wireless …, 2022 - Wiley Online Library
The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is
likely the most severe ailment that necessitates early discovery and treatment, and it …

Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

L Xu, Q Gao, N Yousefi - Simulation, 2020 - journals.sagepub.com
Brain tumors are a group of cancers that originate from different cells of the central nervous
system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a …

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

S Gong, W Gao, F Abza - Computational Intelligence, 2020 - Wiley Online Library
Accurate and early detection of the brain tumor region has a great impact on the choice of
treatment, its success rate, and the follow‐up of the disease process over time. This study …

Defects Identification, Localization, and Classification Approaches: A Review

MKM Chisti, S Srinivas Kumar… - IETE Journal of Research, 2023 - Taylor & Francis
For any industry, an important part of quality control is the detection and identification of
defects of the products. During the manufacturing process, a wide range of defects occur on …

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

S Vigneshwaran, V Govindaraj… - … Journal of Imaging …, 2019 - Wiley Online Library
Human‐made/developed algorithms provide automatic identification and segmentation of
the tissues, lesions and tumor regions available in brain magnetic resonance scan images …

A smartly designed automated map based clustering algorithm for the enhanced diagnosis of pathologies in brain MR images

V Senthilvel, V Govindaraj, YD Zhang… - Expert …, 2021 - Wiley Online Library
The competitive segmentation of fuzzy clustering is utilized in a greater manner to deal with
the local spatial information of input medical images. Fuzzy clustering favours lesions and …

Prognosis of Clinical Depression with Resting State Functionality Connectivity using Machine Learning

S Saranya, N Kavitha - Applications of Artificial Intelligence and Machine …, 2022 - Springer
Abstract Major Depressive Disorder (MDD) is a most prevalent psychiatric disease which
causes functional disabilities resulting in social problems. There is structural and functional …