RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer, Amina; Rani, M. Pushpa; Naz, Saeeda; Razzak, Muhammad Imran; Imran, Muhammad; Xu, Guandong

doi:10.1007/s00521-019-04069-0

Cited by 118 publications

(44 citation statements)

References 52 publications

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“…The deep learning technologies enable computers to take human‐like decisions and make predictions. Deep learning has been successfully applied to many applications like face recognition, speech recognition, text recognition (Naz et al, 2017), writer identification (Rehman, Naz, Razzak, & Hameed, 2019), medical disease predictions (Naseer et al, 2020; Rehman, Naz, Razzak, Akram, & Imran, 2020), machine translation, computer vision, natural language processing, etc. The CNNs deep learners have proved a tremendous success for pattern recognition applications and technologies.…”

Section: Background and Existing Workmentioning

confidence: 99%

Deep OCR for Arabic script‐based language like Pastho

et al. 2020

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Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature-based recognition system for the cursive Pashto script using four pre-trained CNN models using a fine-tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub-word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre-trained models used on the ImageNet Dataset. These models are later fine-tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST-NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.

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Section: Background and Existing Workmentioning

confidence: 99%

Deep OCR for Arabic script‐based language like Pastho

et al. 2020

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“…It is the second most common movement disorder. There is no medical treatment [133,134]; thus, PD patients have to rely on clinical intervention. Das compared different classifiers (DMneural, neural Networks, regression tree and decision Tree) on Max Little dataset [135] for effective diagnosis of Parkinson's diseases.…”

Section: Classificationmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

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137

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Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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“…In medical imaging, deep learning is mostly used to identify damaged parts of any object such as effected part of the lungs, and it is also useful to classify the objects images and prediction models. Application of deep learning approaches in pattern recognition is noticeable as deep learning is nowadays used in many domains like medical analyzation, object recognition system, and object detection [11]- [18]. To identify the different patterns in cell images, the evaluation through deep learning has a significant payback.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

Noreen

Palaniappan

Qayyum³

et al. 2020

IEEE Access

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250

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Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pretrained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. INDEX TERMS Deep learning, magnetic resonance imaging, brain tumor classification, pre-trained model, dataset.

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RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Cited by 118 publications

References 52 publications

Deep OCR for Arabic script‐based language like Pastho

Deep OCR for Arabic script‐based language like Pastho

Big data analytics for preventive medicine

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

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