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Article: A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.

Arya, Akhilesh Deep / Verma, Sourabh Singh / Chakarabarti, Prasun / Chakrabarti, Tulika / Elngar, Ahmed A / Kamali, Ali-Mohammad / Nami, Mohammad

Brain informatics

2023 Volume 10, Issue 1, Page(s) 17

Abstract: ... used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning ... information from high-dimensional data. Researchers use deep learning models in the field of medicine ... a systematic review of publications using machine learning and deep learning methods for early classification ...

Abstract	Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.
Language	English
Publishing date	2023-07-14
Publishing country	Germany
Document type	Journal Article
ISSN	2198-4018
ISSN	2198-4018
DOI	10.1186/s40708-023-00195-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Quantum Deep Hedging

Cherrat, El Amine / Raj, Snehal / Kerenidis, Iordanis / Shekhar, Abhishek / Wood, Ben / Dee, Jon / Chakrabarti, Shouvanik / Chen, Richard / Herman, Dylan / Hu, Shaohan / Minssen, Pierre / Shaydulin, Ruslan / Sun, Yue / Yalovetzky, Romina / Pistoia, Marco

2023

Abstract: ... in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers ...

Abstract	Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.
Keywords	Quantum Physics ; Computer Science - Machine Learning ; Quantitative Finance - Computational Finance
Subject code	006
Publishing date	2023-03-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

Akhilesh Deep Arya / Sourabh Singh Verma / Prasun Chakarabarti / Tulika Chakrabarti / Ahmed A. Elngar / Ali-Mohammad Kamali / Mohammad Nami

Brain Informatics, Vol 10, Iss 1, Pp 1-

2023 Volume 15

Abstract: ... used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning ... information from high-dimensional data. Researchers use deep learning models in the field of medicine ... a systematic review of publications using machine learning and deep learning methods for early classification ...

Abstract	Abstract Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.
Keywords	Alzheimer’s disease ; Dementia ; MCI ; Neurodegenerative ; Machine learning ; Deep learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Computer software ; QA76.75-76.765
Subject code	006
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Deep learning-based image processing in optical microscopy.

Melanthota, Sindhoora Kaniyala / Gopal, Dharshini / Chakrabarti, Shweta / Kashyap, Anirudh Ameya / Radhakrishnan, Raghu / Mazumder, Nirmal

Biophysical reviews

2022 Volume 14, Issue 2, Page(s) 463–481

Language	English
Publishing date	2022-04-06
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2486483-3
ISSN	1867-2469 ; 1867-2450
ISSN (online)	1867-2469
ISSN	1867-2450
DOI	10.1007/s12551-022-00949-3
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Article ; Online: Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

Jin, Xue-Bo / Wang, Zhong-Yao / Kong, Jian-Lei / Bai, Yu-Ting / Su, Ting-Li / Ma, Hui-Jun / Chakrabarti, Prasun

Entropy (Basel, Switzerland)

2023 Volume 25, Issue 2

Abstract: The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A ... ...

Abstract	The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.
Language	English
Publishing date	2023-01-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e25020247
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Article ; Online: Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Lilhore, Umesh Kumar / Dalal, Surjeet / Varshney, Neeraj / Sharma, Yogesh Kumar / Rao, K B V Brahma / Rao, V V R Maheswara / Alroobaea, Roobaea / Simaiya, Sarita / Margala, Martin / Chakrabarti, Prasun

Scientific reports

2024 Volume 14, Issue 1, Page(s) 4533

Abstract: ... demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep ...

Abstract	Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.
MeSH term(s)	Humans ; Female ; Depression, Postpartum/diagnosis ; Depression, Postpartum/epidemiology ; Prevalence ; Deep Learning ; Risk Factors ; Depressive Disorder
Language	English
Publishing date	2024-02-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-54927-8
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Article ; Online: Deep phenotyping and genomic data from a nationally representative study on dementia in India.

Lee, Jinkook / Petrosyan, Sarah / Khobragade, Pranali / Banerjee, Joyita / Chien, Sandy / Weerman, Bas / Gross, Alden / Hu, Peifeng / Smith, Jennifer A / Zhao, Wei / Aksman, Leon / Jain, Urvashi / Shanthi, G S / Kurup, Ravi / Raman, Aruna / Chakrabarti, Sankha Shubhra / Gambhir, Indrajeet Singh / Varghese, Mathew / John, John P /

Joshi, Himanshu / Koul, Parvaiz A / Goswami, Debabrata / Talukdar, Arunansu / Mohanty, Rashmi Ranjan / Yadati, Y Sathyanarayana Raju / Padmaja, Mekala / Sankhe, Lalit / Rajguru, Chhaya / Gupta, Monica / Kumar, Govind / Dhar, Minakshi / Jovicich, Jorge / Ganna, Andrea / Ganguli, Mary / Chatterjee, Prasun / Singhal, Sunny / Bansal, Rishav / Bajpai, Swati / Desai, Gaurav / Bhatankar, Swaroop / Rao, Abhijith R / Sivakumar, Palanimuthu T / Muliyala, Krishna Prasad / Sinha, Preeti / Loganathan, Santosh / Meijer, Erik / Angrisani, Marco / Kim, Jung Ki / Dey, Sharmistha / Arokiasamy, Perianayagam / Bloom, David E / Toga, Arthur W / Kardia, Sharon L R / Langa, Kenneth / Crimmins, Eileen M / Dey, Aparajit B

Scientific data

2023 Volume 10, Issue 1, Page(s) 45

Abstract: The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures ... ...

Abstract	The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia.
MeSH term(s)	Aged ; Humans ; Aging ; Cognitive Dysfunction ; Dementia/genetics ; Genomics ; Longitudinal Studies ; India
Language	English
Publishing date	2023-01-20
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-023-01941-6
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Article ; Online: Antiplasmodial Compounds from Deep-Water Marine Invertebrates.

Wright, Amy E / Collins, Jennifer E / Roberts, Bracken / Roberts, Jill C / Winder, Priscilla L / Reed, John K / Diaz, Maria Cristina / Pomponi, Shirley A / Chakrabarti, Debopam

Marine drugs

2021 Volume 19, Issue 4

Abstract: Novel drug leads for malaria therapy are urgently needed because of the widespread emergence of resistance to all available drugs. Screening of the Harbor Branch enriched fraction library against ... ...

Abstract	Novel drug leads for malaria therapy are urgently needed because of the widespread emergence of resistance to all available drugs. Screening of the Harbor Branch enriched fraction library against the
MeSH term(s)	Animals ; Anthozoa/metabolism ; Antimalarials/isolation & purification ; Antimalarials/pharmacology ; Diterpenes/isolation & purification ; Diterpenes/pharmacology ; Hep G2 Cells ; Humans ; Life Cycle Stages ; Malaria, Falciparum/drug therapy ; Malaria, Falciparum/parasitology ; Molecular Structure ; Plasmodium falciparum/drug effects ; Plasmodium falciparum/growth & development ; Porifera/metabolism ; Structure-Activity Relationship ; Time Factors
Chemical Substances	Antimalarials ; Diterpenes
Language	English
Publishing date	2021-03-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175190-0
ISSN	1660-3397 ; 1660-3397
ISSN (online)	1660-3397
ISSN	1660-3397
DOI	10.3390/md19040179
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Article ; Online: A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Supakar, Rinku / Satvaya, Parthasarathi / Chakrabarti, Prasun

Computers in biology and medicine

2022 Volume 151, Issue Pt A, Page(s) 106225

Abstract: ... that are indicative of schizophrenia. Since deep learning is capable of automatically extracting ... the significant features and make classifications, the authors proposed a deep learning based model using RNN-LSTM ...

Abstract	Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep learning is capable of automatically extracting the significant features and make classifications, the authors proposed a deep learning based model using RNN-LSTM to analyze the EEG signal data to diagnose schizophrenia. The proposed model used three dense layers on top of a 100 dimensional LSTM. EEG signal data of 45 schizophrenic patients and 39 healthy subjects were used in the study. Dimensionality reduction algorithm was used to obtain an optimal feature set and the classifier was run with both sets of data. An accuracy of 98% and 93.67% were obtained with the complete feature set and the reduced feature set respectively. The robustness of the model was evaluated using model performance measure and combined performance measure. Outcomes were compared with the outcome obtained with traditional machine learning classifiers such as Random Forest, SVM, FURIA, and AdaBoost, and the proposed model was found to perform better with the complete dataset. When compared with the result of the researchers who worked with the same set of data using either CNN or RNN, the proposed model's accuracy was either better or comparable to theirs.
MeSH term(s)	Humans ; Deep Learning ; Schizophrenia/diagnosis ; Electroencephalography/methods ; Algorithms ; Machine Learning
Language	English
Publishing date	2022-10-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.106225
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Article ; Online: Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Wahlang, Imayanmosha / Maji, Arnab Kumar / Saha, Goutam / Chakrabarti, Prasun / Jasinski, Michal / Leonowicz, Zbigniew / Jasinska, Elzbieta

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 5

Abstract: ... In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender ... and age are added as higher attributes for more accurate and meaningful classification. A deep ... learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also ...

Abstract	Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.
MeSH term(s)	Brain/diagnostic imaging ; Deep Learning ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Support Vector Machine
Language	English
Publishing date	2022-02-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22051766
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