Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy

Devunooru, Sindhu; Alsadoon, Abeer; Chandana, P. W. C.; Beg, Azam

doi:10.1007/s12652-020-01998-w

Cited by 45 publications

(22 citation statements)

References 52 publications

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“…Devunooru et al conducted a study for use in the diagnosis of brain tumors. They performed the segmentation process of my brain MRI images using deep learning methods and obtained successful results [39]. Göceri has conducted a study that will automate the classification of dermatological diseases.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

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Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

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Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

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“…Deep learning has become popular in recent years [23,19,16,24,25], especially with the rapid development of graphics processing technology. Some of the many applications of implementing this procedure are image processing [26,27,28], medical diagnoses [29] and gene expression classification [30]. Recently, they have also been used successfully for gravitational wave astronomy in the form of classification of gravitational wave signal errors [23] in which it was demonstrated that deep learning can be used as a detection method [19] and estimation of source parameters.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Characterization of Gravitational Waves Signals Using Neural Networks

Caramete,

Constantinescu,

Caramete

et al. 2020

Preprint

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“…Medical image segmentation is of great significance in medical imaging, as various areas of image processing, object detection, and visual observation are intersecting. In the image processing field, segmentation, the isolation of the objects of interest from their background and with each other seems to be a central research mechanism by which new approaches are being formed [1]. In diagnostic imaging, automatic delineation of various image attributes is used for the analysis of morphological characters and forms of tissues, the spatial pattern of structure and action, as well as biological aspects, are addressed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Network for Thoracic Organs Segmentation

Khalil¹,

Tehsin²,

Humayun³

et al. 2022

Computers, Materials &Amp; Continua

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Medical Imaging Segmentation is an essential technique for modern medical applications. It is the foundation of many aspects of clinical diagnosis, oncology, and computer-integrated surgical intervention. Although significant successes have been achieved in the segmentation of medical images, DL (deep learning) approaches. Manual delineation of OARs (organs at risk) is vastly dominant but it is prone to errors given the complex irregularities in shape, low texture diversity between tissues and adjacent blood area, patientwide location of organisms, and weak soft tissue contrast across adjacent organs in CT images. Till now several models have been implemented on multi organs segmentation but not caters to the problem of imbalanced classes some organs have relatively small pixels as compared to others. To segment OARs in thoracic CT images, we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model. We have built a fully connected CNN (Convolutional Neural network) having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs. Proposed methodology achieves 0.93405 IOU score, 0.95138 F1 score and class-wise dice score for esophagus 0.92466, trachea 0.94257, heart 0.95038, aorta 0.9351 and background 0.99891. The results showed that our proposed framework can be segmented organs accurately.

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Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy

Cited by 45 publications

References 52 publications

Diagnosing Hematological Disorders Using Deep Learning Method

Diagnosing Hematological Disorders Using Deep Learning Method

Characterization of Gravitational Waves Signals Using Neural Networks

Multi-Scale Network for Thoracic Organs Segmentation

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