Brain MRI analysis using a deep learning based evolutionary approach

Shahamat, Hossein; Abadeh, Mohammad Saniee

doi:10.1016/j.neunet.2020.03.017

Cited by 83 publications

(51 citation statements)

References 43 publications

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“…To indicate the results briefly, in one case study, a 90.9% accuracy is gotten to classify three grades of glioma in different case study, Pituitary, Meningioma, and Glioma tumor types are categorized with the total accuracy at 94.2%. Shahamat and Abadeh [29], introduced 3D-CNN for classifying brain magnetic resonance imaging into two pre-determined classifications. Moreover, a method of genetic algorithm based brain masking was suggested as a visualization technique providing a clear understanding to three-dimension convolutional neural network function.…”

Section: Related Workmentioning

confidence: 99%

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

Ahmed

Salah

2022

IJEECS

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The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

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Section: Related Workmentioning

confidence: 99%

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

Ahmed

Salah

2022

IJEECS

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“…At the same time, it is required that the core information of the original data will not be lost in the new space, and the redundant components should be removed. Its core work is to find a set of orthogonal transformation bases in the original space and construct a new coordinate system in the new space with the variance of the original data as a reference [ 14 , 15 ]. Data dimensionality reduction is achieved by mapping N-dimensional features to k-dimensional features.…”

Section: Design Of Deep Neural Network Architecturementioning

confidence: 99%

Internet of things-enabled real-time health monitoring system using deep learning

Liu

Wang

et al. 2021

Neural Comput & Applic

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Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes’ life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes’ conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.

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“…Clinical prior knowledge was also inserted into the attention mechanism to make the whole system more transparent. For instance, [85] split brain MRIs into 96 clinically important regions and used a genetic algorithm to calculate the importance of each region to evaluate Alzheimer's Disease (AD).…”

Section: In: Interpretabilitymentioning

confidence: 99%

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

Chen¹,

Gómez²,

Huang³

et al. 2021

Preprint

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Transparency in Machine Learning (ML), including interpretable or explainable ML, attempts to reveal the working mechanisms of complex models, including deep neural networks. Transparent ML promises to advance human factors engineering goals of human-centered AI, such as increasing trust or reducing automation bias, in the target users. However, a core requirement in achieving these aims is that the method is indeed transparent to the users. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i. e., a relationship between algorithm and user; as a result, iterative prototyping and evaluation with users is critical to attaining adequate solutions that afford transparency. However, following human-centered design principles in highly specialized and high stakes domains, such as healthcare and medical image analysis, is challenging due to the limited availability of and access to end users, such as domain experts, providers, or patients. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and those end users, which implies an even greater need for iterated development.To investigate the state of transparent ML in medical image analysis, we conducted and now present a systematic review of the literature. Our review reveals multiple severe shortcomings in the design and validation of transparent ML for medical image analysis applications. We find that most studies to date approach transparency as a property of the model itself, similar to task performance, without considering end users during neither development nor evaluation. Despite the considerable difference between the roles and knowledge of ML developers and clinical stakeholders, no study reported formative user research to inform the design and development of transparent ML models; moreover, only a few studies validated transparency claims through empirical user evaluations. The oversight of considering ML transparency as a relationship with end users, the lack of user research, and the sporadic validation of transparency claims put contemporary research on transparent ML for medical image analysis at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research while acknowledging the challenges of human-centered design in healthcare, we introduce the INTRPRT guideline, a systematic design directive for transparent ML systems in medical image analysis. To bridge the disconnect between ML system designers and end users and avoid misspending costly development efforts on models that are finally validated as non-transparent to end users, the INTRPRT guideline suggests formative user research as the first step of transparent model design to understand user needs and domain requirements. Following this process produces evidence to support * Joint first authors.Preprint. Under review.

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Brain MRI analysis using a deep learning based evolutionary approach

Cited by 83 publications

References 43 publications

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

Internet of things-enabled real-time health monitoring system using deep learning

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

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