Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

Amaya-Rodriguez, Isabel; Durán-López, Lourdes; Luna-Perejón, Francisco; Civit-Masot, Javier; Domínguez-Morales, Juan Pedro; Vicente, Sara; Balcells, A; Cascado, D.; Linares-Barranco, A.

doi:10.5220/0008494005280535

Cited by 5 publications

(4 citation statements)

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“…For the first fusion form, fuzzy C-means improves the preprocessing result of the input MRI dataset, and CNNs are used for the diagnosis of glioma detection (Amaya-Rodriguez, Duran-Lopez et al 2019). Similarly, fuzzy C-means are adopted for image segmentation during preprocessing (Kim, Cho et al 2019, Sevik, Karakullukcu et al 2019.…”

Section: Fuzzy Convolutional Neural Networkmentioning

confidence: 99%

“…The signal in MR image usually comes from the protons in fat and water molecules in the body, so that it is effective on soft tissues with anatomical information. To detect glioma, a type of brain tumor, CNNs are used for diagnosis aids and fuzzy C-means improves the method for preprocessing the input MRI dataset (Amaya-Rodriguez, Duran-Lopez et al 2019). Based on heart MRI dataset, an innovative hybrid algorithm is proposed to address noisy data, by combining hybrid ant colony, cat fuzzy neural model and African buffalo optimization (Doppala, Bhattacharyya et al 2020).…”

Section: Uncertain Medical Data 321 Imagesmentioning

confidence: 99%

“…A state-of-the-art survey paper summarizes the research achievements about the fusion of deep learning and fuzzy systems in recent years, and represents the overall framework and graphical form of fusing deep learning and fuzzy systems, as well as some challenges and future research . Especially, fuzzy deep learning has been applied to handle uncertain medical data, e.g., images (Amaya-Rodriguez, Duran-Lopez et al 2019, Ramirez, Sanchez et al 2019, Mohebbian, Hassan et al 2020, Huang, Zhang et al 2021), text records (Davoodi and Moradi 2018, Gangavarapu, Jayasimha et al 2019, Li, Wei et al 2021, Poli, Ouerdane et al 2021) and video files (Lawanot, Inoue et al 2019, Fathabadi, Grantner et al 2021, Verma and Dubey 2021, for different effects, segmentation (Chouhan, Kaul et al 2018, Sevik, Karakullukcu et al 2019, classification (Luo, Pan et al 2020, Zhuang, Kang et al 2020, natural language processing (Davoodi andMoradi 2018, Gangavarapu, Jayasimha et al 2019), prediction (Jiang, Li et al 2021, Nguyen, Kavuri et al 2022) and fusion (Hermessi, Mourali et al 2019, Vinnarasi, Daniel et al 2021 This paper aims to review fuzzy deep learning for uncertain medical data. The main contributions of the paper are listed as follows: 1) constructing four types of frameworks of fuzzy deep learning methods used for uncertain medical data; 2) making a survey of fuzzy deep learning for uncertain medical data, including widely-used fuzzy deep learning methods, uncertain medical data and effects; 3) exhibiting evaluation metrics considering classification, segmentation and fusion; 4) providing some critical discussions on advantages, challenges and future research directions of fuzzy deep learning for uncertain medical data.…”

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confidence: 99%

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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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Section: Fuzzy Convolutional Neural Networkmentioning

confidence: 99%

Section: Uncertain Medical Data 321 Imagesmentioning

confidence: 99%

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confidence: 99%

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A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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“…In the last decade, convolutional neural networks [ 13 ] have experimented a rapid development, as the number of researchers using them for medical image processing grows, in systems where performance is an important factor [ 14 , 15 , 16 , 17 ]. Specifically, for brain MRI processing, convnets have been successfully applied in segmentation and classification tasks, predicting the stage of Alzheimer’s disease [ 18 ], cerebellum [ 4 ] and brain parcellation [ 19 ], and tumor detection and segmentation [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging

Cabeza-Ruiz

Velázquez‐Pérez²,

Linares-Barranco

et al. 2022

Sensors

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The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.

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