Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images

Kaplan, Ela; Altunışık, Erman; Fırat, Yasemin Ekmekyapar; Barua, Prabal Datta; Doğan, Şengül; Bayğın, Mehmet; Demir, Fahrettin Burak; Tuncer, Türker; Palmer, Elizabeth E.; Tan, Ru‐San; Yu, Ping; Soar, Jeffrey; Fujita, Hamido; Acharya, U. Rajendra

doi:10.1016/j.cmpb.2022.107030

Cited by 28 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ela Kuplan et al (2022) [6] adopted a novel method for the classification of symptoms of PD using MRI scans. The main goal of the study was to explore more clinical data to elaborate the efficacy of artificial intelligence for better detection of the PD disease.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

et al. 2023

View full text Add to dashboard Cite

Parkinson's disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient's productive and healthy lives. The disease's symptoms are characterized by tremors, muscle rigidity, slowness in movements, balancing along with other psychiatric symptoms. The dynamics of handwritten records served as one of the dominant mechanisms which support PD detection and assessment. Several machine learning methods have been investigated for the early detection of this disease. But, most of these handcrafted feature extraction techniques predominantly suffer from low performance accuracy issues. This cannot be tolerable for dealing with detection of such a chronic ailment. To this end, an efficient deep learning model is proposed which can assist to have early detection of Parkinson's disease. The significant contribution of the proposed model is to select the most optimum features which have the effect of getting the high performance accuracies. The feature optimization is done through genetic algorithm wherein K-Nearest Neighbour technique. The proposed novel model results into detection accuracy higher than 95%, precision of 98%, area under curve of 0.90 with a loss of 0.12 only. The performance of proposed model is compared with some state of the art machine learning and deep learning based PD detection approaches to demonstrate the better detection ability of our model.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Machine learning-based models are used in the literature for many different disciplines. [16][17][18] State-of-the-art automated models developed for the diagnosis of COVID-19 using CXR and CT images are summarized in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Erdem,

Kobat,

Bilen

et al. 2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.

show abstract

“…In this regard, data mining/deep learning methods and algorithms can be useful in extracting meaningful information from data and identifying patients from healthy people. [18][19][20] This paper will propose an efficient deep learning architecture for diagnosing Parkinson's patients from normal ones. We tried to automatically review the hand drawing images of people who took the Parkinson's test using digital pen devices (as illustrated in Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing of Parkinson's disease based on hand drawing analysis using Bi‐Directional LSTM equipped with fuzzy inferential soft‐max classifier

Ramzani,

Yadollahzadeh‐Tabari,

GolesorkhtabarAmiri

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison.

show abstract

Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images

Cited by 28 publications

References 40 publications

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Diagnosing of Parkinson's disease based on hand drawing analysis using Bi‐Directional LSTM equipped with fuzzy inferential soft‐max classifier

Contact Info

Product

Resources

About