Performance Analysis of Different Machine Learning Classifiers in Detection of Parkinson’s Disease from Hand-Drawn Images Using Histogram of Oriented Gradients

Das, Akalpita; Das, Himanish Shekhar; Neog, Anupal; Reddy, B. Bharat; Swargiary, Mrinoy

doi:10.1007/978-981-33-4604-8_16

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Zhao et al [86] created the social media nested epidemic simulation (SimNest) using online semi-supervised DL. In [87] and [88], Das et al proposed how Parkinson's disease is detected from handdrawn images using DL. In [89], Das et al also proposed how breast cancer is detected from mammogram images using DL.…”

Section: Deep Learning In the Biomedical Applicationsmentioning

confidence: 99%

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis

Mukherji,

Mondal,

Banerjee

et al. 2022

AIA

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Background: Consecutive Clustering is one type of learning method that is built on neural network. It is frequently used in different domains including biomedical research. It is very useful for consecutive clustering (adjacent clustering). Adjacent clustering is highly used where there are various specific locations or addresses denoting each individual features in the data that need to be grouped consecutively. One of the useful consecutive clustering in the field of biomedical research is differentially methylated region (DMR) finding analysis on various CpG sites (features).Method: So far, many researches have been carried out on deep learn-ing and consecutive clustering in biomedical domain. But for epigenetics study, very limited survey papers have been published till now where con-secutive clustering has been demonstrated together. Hence, in this study, we contributed a comprehensive survey on several fundamental categories of consecutive clustering, e.g., Convolutional Neural Network(CNN) Auto-Encoder (AE), Restricted Boltzmann Machines (RBM) and Deep Belief Net-work (DBN), Recurrent Neural Network (RNN), Deep Stacking Networks (DSN), Long Short Term Memory (LSTM) / Gated Recurrent Unit (GRU) Network etc., along with their applications, advantages and disadvantages. Different forms of consecutive clustering algorithms are covered in the second section (viz., supervised and unsupervised DMR finding methods) used for DNA methylation data have been described here along with their advantages, shortcomings and overall performance estimation (power, time). Conclusion:Our survey paper provides a latest research work that have been done for consecutive clustering algorithms for healthcare purposes. All the usages, benefits and shortcomings along with their performance evaluation of each algorithm has been elaborated in our manuscript by which new biomedical researchers can understand and use those tools and algorithms for their research prospective.

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Section: Deep Learning In the Biomedical Applicationsmentioning

confidence: 99%

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis

Mukherji,

Mondal,

Banerjee

et al. 2022

AIA

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“…Deep learning has made significant strides in research over the past 10 years ( Das et al, 2021 ), and the subject of healthcare is no exception ( Das et al, 2020 ). In the research on breast cancer, which produced encouraging findings as well, several deep architectures ( Hamidinekoo et al, 2018 ) are investigated and effectively applied.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

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Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2.Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively.Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

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A Transfer Learning Approach with MobileNetV2 for Parkinson’s Disease Detection using Hand-Drawings

Kumar,

Bansal

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Performance Analysis of Different Machine Learning Classifiers in Detection of Parkinson’s Disease from Hand-Drawn Images Using Histogram of Oriented Gradients

Cited by 5 publications

References 11 publications

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

A Transfer Learning Approach with MobileNetV2 for Parkinson’s Disease Detection using Hand-Drawings

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