Neurodegenerative disease detection and severity prediction using deep learning approaches

Erdaş, Çağatay Berke; Sümer, Emre; Kibaroğlu, Seda

doi:10.1016/j.bspc.2021.103069

Cited by 24 publications

(4 citation statements)

References 30 publications

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“…To obtain accurate performance assessment, the models were evaluated using 10-fold crossvalidation. [ 13 ] A batch size of 64 was used, along with a learning rate of 0.01 and 10 epochs.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of the system was assessed using various metrics, such as accuracy, F1 score, Matthew's correlation coefficient (MCC), receiver operating characteristic (ROC), sensitivity, specificity, and precision. [ 13 ] As can be seen in Equation 1, accuracy indicates how frequently the system accurately predicts outcomes and is determined by dividing the number of correct predictions by the total number of predictions made.…”

Section: Resultsmentioning

confidence: 99%

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Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs

Berke Erdaş

2023

Jt Dis Relat Surg

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Objectives This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs. Materials and methods The data set used in the retrospective study consisted of different types of upper extremity radiographs obtained from an open-source dataset, with 4,480 images with fractures and 4,383 images without fractures. All fractures involved the ulna or radius. The proposed method comprises two distinct stages. The initial phase, referred to as preprocessing, involved the removal of radiographic backgrounds, followed by the elimination of nonbone tissue. In the second phase, images consisting only of bone tissue were processed using deep learning models, such as RegNetX006, EfficientNet B0, and InceptionResNetV2. Thus, whether one or more fractures of the ulna or the radius are present was determined. To measure the performance of the proposed method, raw images, images generated by background deletion, and bone tissue removal were classified separately using RegNetX006, EfficientNet B0, and InceptionResNetV2 models. Performance was assessed by accuracy, F1 score, Matthew’s correlation coefficient, receiver operating characteristic area under the curve, sensitivity, specificity, and precision using 10-fold cross-validation, which is a widely accepted technique in statistical analysis. Results The best classification performance was obtained with the proposed preprocessing and RegNetX006 architecture. The values obtained for various metrics were as follows: accuracy (0.9921), F1 score (0.9918), Matthew's correlation coefficient (0.9842), area under the curve (0.9918), sensitivity (0.9974), specificity (0.9863), and precision (0.9923). Conclusion The proposed preprocessing method is able to detect fractures of the ulna and radius by artificial intelligence.

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“…To obtain accurate performance assessment, the models were evaluated using 10-fold crossvalidation. [ 13 ] A batch size of 64 was used, along with a learning rate of 0.01 and 10 epochs.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs

Berke Erdaş

2023

Jt Dis Relat Surg

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“…Com o avanc ¸o da Inteligência Artificial (IA) e a capacidade de processar grandes conjuntos de dados, de maneira rápida e eficaz, diversos sistemas inteligentes para auxiliar no diagnóstico de doenc ¸as tem surgido. Dentre as aplicac ¸ões de IA para este fim, pode-se citar aquelas focadas no diagnóstico de doenc ¸as pulmonares, como COVID-19, tuberculose e pneumonia [Jaeger et al 2013, Li et al 2020, Abideen et al 2020, C ¸allı et al 2021, doenc ¸as cardiovasculares [Ghumbre and Ghatol 2012], e também doenc ¸as neurodegenerativas [Dutta et al 2009, Ning et al 2018, Erdas ¸et al 2021, Fraiwan and Hassanin 2021.…”

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Avaliando a Sobreamostragem de Dados Temporais de Marcha no Diagnóstico Automático de Doenças Neurodegenerativas

Chagas,

Bucci,

Félix

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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Doenças neurodegenerativas (DNDs) causam, dentre outros sintomas, o comprometimento da marcha. Diversos estudos analisam a marcha para, com o auxílio da inteligência artificial, auxiliar no diagnóstico de DNDs. Devido à dificuldade de coleta de novos dados, a técnica de sobreamostragem através do janelamento de dados é frequentemente utilizada. No entanto, um estudo anterior apontou para um possível enviesamento na fase de treinamento de algoritmos de classificação utilizando técnicas de janelamento. Este trabalho investiga esse viés, avaliando, além da validação cruzada tradicionalmente utilizada, uma segunda abordagem, em que tratamos o enviesamento apontado. Os resultados indicam a necessidade de cuidados extras quando se lida com sobreamostragem de dados temporais de marcha.

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“…Convolutional neural networks are the most commonly used algorithm in DL for pattern recognition. These algorithms utilise image data to learn patterns and apply classification techniques to classify patterns based on types and features [28]. They detect spatial and temporal features from the database, maximising the feasibility of the recognition process [29].…”

Section: Introductionmentioning

confidence: 99%

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification

Anjum

Shahab

2023

Diagnostics

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Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.

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Neurodegenerative disease detection and severity prediction using deep learning approaches

Cited by 24 publications

References 30 publications

Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs

Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs

Avaliando a Sobreamostragem de Dados Temporais de Marcha no Diagnóstico Automático de Doenças Neurodegenerativas

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification

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