A Deep Learning-Based Comparative Study to Track Mental Depression from EEG Data

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

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VGG16, ResNet-50, and GoogLeNet Deep Learning Architecture for Breathing Sound Classification: A Comparative Study

10.1109/ai-csp52968.2021.9671124 ◽

2021 ◽

Author(s):

Neili Zakaria ◽

Fezari Mohamed ◽

Redjati Abdelghani ◽

Kenneth Sundaraj

Keyword(s):

Deep Learning ◽

Comparative Study ◽

Sound Classification

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Ahed: A Heterogeneous Domain Deep Learning Model for IoT Enabled Smart Health with Few-labeled EEG Data

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3105647 ◽

2021 ◽

pp. 1-1

Author(s):

Lei Chu ◽

Ling Pei ◽

Robert Qiu

Keyword(s):

Deep Learning ◽

Learning Model ◽

Eeg Data ◽

Smart Health ◽

Deep Learning Model

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An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9437 ◽

2020 ◽

Vol 17 (12) ◽

pp. 5438-5446

Author(s):

C. Suguna ◽

S. P. Balamurugan

Keyword(s):

Machine Learning ◽

Cervical Cancer ◽

Deep Learning ◽

Comparative Study ◽

Cancer Diagnosis ◽

Pap Smear ◽

Classification Models ◽

Extensive Review ◽

Diagnosis And Classification ◽

Automated Machine Learning

Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.

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