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Article ; Online: MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management.

Monga, Suhasini / Singh, Dilbag

2022 Volume 12, Issue 1, Page(s) 17660

Abstract: Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is ...

Abstract	Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information. Medical care information requires robust security and privacy. Also there are other issues like confidentiality, interoperability, scalability, cost efficiency and timeliness that need to be addressed. To achieve these objectives, this paper proposes a novel-scalable patient centric yet privacy preserving framework for efficient and secure electronic medical records management. In addition, proposed system generates a unified trusted record and authentication role mapping for enforcing secure access control for medical records using complex encryption algorithms. This paper identifies 13 key performance factors for performance comparison of proposed framework with traditional models. Ethereum and Binance Smart Chain acted as a benchmark platform for performance evaluation of MRBSChain on the basis of three metrics (transaction cost, average block time and deployment cost).At last, a comparative analysis of MRBSChain with other state of art blockchain systems on the basis of execution time is presented in the paper.
MeSH term(s)	Female ; Humans ; Male ; Computer Security ; Ecosystem ; Blockchain ; Electronic Health Records ; Forms and Records Control
Language	English
Publishing date	2022-10-21
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/s41598-022-22569-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Singh, Dilbag / Alzubi, Ahmad Ali / Kaur, Manjit / Kumar, Vijay / Lee, Heung-No

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, ... ...

Abstract	Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3377631
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Article ; Online: Parthenium hysterophorus derived nanostructures as an efficient carbocatalyst for the electrochemical sensing of mercury(II) ions.

Sharma, Ritika / Rana, Dharmender Singh / Gupta, Neeraj / Thakur, Sourbh / Thakur, Kamal Kishor / Singh, Dilbag

Chemosphere

2024 Volume 354, Page(s) 141591

Abstract: The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in ... ...

Abstract	The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in the present work, we have made the effective management of Parthenium weeds by converting them into carbon-based nanomaterial through hydrothermal treatment followed by heating in a tube furnace under the nitrogen atmosphere. The XPS studies confirm the natural presence of nitrogen and oxygen-containing functional groups in the biomass-derived carbon. The nanostructure has adopted a layered two-dimensional structure, clearly indicated through HRTEM images. Further, the nanomaterials are analyzed for their ability towards the electrochemical detection of mercury, with a detection limit of 6.17 μM, while the limit of quantification and sensitivity was found to be 18.7 μM and 0.4723 μM μA
MeSH term(s)	Mercury ; Parthenium hysterophorus ; Biosensing Techniques/methods ; Nanostructures/chemistry ; Carbon/chemistry ; Ions ; Nitrogen/chemistry ; Oxygen
Chemical Substances	Mercury (FXS1BY2PGL) ; Carbon (7440-44-0) ; Ions ; Nitrogen (N762921K75) ; Oxygen (S88TT14065)
Language	English
Publishing date	2024-03-07
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2024.141591
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Article: Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

B, Ashwini / Kaur, Manjit / Singh, Dilbag / Roy, Satyabrata / Amoon, Mohammed

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 20

Abstract: Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor ... ...

Abstract	Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.
Language	English
Publishing date	2023-10-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13203234
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Article ; Online: MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

Kaur, Manjit / Singh, Dilbag / Kumar, Vijay / Lee, Heung-No

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 10, Page(s) 5004–5014

Abstract: One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated ... ...

Abstract	One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.
MeSH term(s)	Female ; Humans ; Uterine Cervical Neoplasms/diagnosis ; Deep Learning ; Benchmarking ; Exercise ; Neck
Language	English
Publishing date	2023-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3223127
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Zs.A 5483: Show issues

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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Pachydermoperiostosis with Hearing Loss.

Singh, Dilbag / Rawat, Ritu / Thakur, Vishal

Skinmed

2022 Volume 20, Issue 4, Page(s) 311–313

Abstract: A 21-year-old unmarried man, born of a non-consanguineous marriage, presented to the dermatology department with progressive thickening of the facial skin and eyelids, plus increased folds over his forehead for the last 5 months. He also complained of ... ...

Abstract	A 21-year-old unmarried man, born of a non-consanguineous marriage, presented to the dermatology department with progressive thickening of the facial skin and eyelids, plus increased folds over his forehead for the last 5 months. He also complained of progressive enlargement of his hands and feet, with intermittent joint pains in his wrists, elbows, and ankles, along with occasional abdominal pain. He had a hearing loss and increased sweating. (
MeSH term(s)	Adult ; Arthralgia ; Face ; Hearing Loss ; Humans ; Male ; Osteoarthropathy, Primary Hypertrophic/complications ; Osteoarthropathy, Primary Hypertrophic/diagnosis ; Skin ; Young Adult
Language	English
Publishing date	2022-08-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2171125-2
ISSN	1751-7125 ; 1540-9740
ISSN (online)	1751-7125
ISSN	1540-9740
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Zs.A 6041: Show issues

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Article: Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks.

Kaur, Manjit / Singh, Dilbag

Journal of ambient intelligence and humanized computing

2020 Volume 12, Issue 2, Page(s) 2483–2493

Abstract: The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more ... ...

Abstract	The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.
Keywords	covid19
Language	English
Publishing date	2020-08-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2543187-0
ISSN	1868-5145 ; 1868-5137
ISSN (online)	1868-5145
ISSN	1868-5137
DOI	10.1007/s12652-020-02386-0
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Article: DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Kaur, Manjit / AlZubi, Ahmad Ali / Jain, Arpit / Singh, Dilbag / Yadav, Vaishali / Alkhayyat, Ahmed

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 17

Abstract: Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To ... ...

Abstract	Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.
Language	English
Publishing date	2023-08-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13172752
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Article ; Online: Efficient Evolving Deep Ensemble Medical Image Captioning Network.

Singh, Dilbag / Kaur, Manjit / Alanazi, Jazem Mutared / AlZubi, Ahmad Ali / Lee, Heung-No

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 2, Page(s) 1016–1025

Abstract: With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion ... ...

Abstract	With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.
MeSH term(s)	Humans ; Artificial Intelligence ; Image Processing, Computer-Assisted ; Diagnostic Imaging
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3223181
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Article ; Online: Development of metal free carbon catalyst derived from Parthenium hysterophorus for the electrochemical detection of dopamine.

Rana, Dharmender Singh / Sharma, Ritika / Gupta, Neeraj / Sharma, Vinit / Thakur, Sourbh / Singh, Dilbag

Environmental research

2023 Volume 231, Issue Pt 2, Page(s) 116151

Abstract: Parthenium hysterophorus, one of the seven most hazardous weeds is widely known for its allergic, respiratory and skin-related disorders. It is also known to affect biodiversity and ecology. For eradication of the weed, its effective utilization for the ... ...

Abstract	Parthenium hysterophorus, one of the seven most hazardous weeds is widely known for its allergic, respiratory and skin-related disorders. It is also known to affect biodiversity and ecology. For eradication of the weed, its effective utilization for the successful synthesis of carbon-based nanomaterial is a potent management strategy. In this study, reduced graphene oxide (rGO) was synthesized from weed leaf extract through a hydrothermal-assisted carbonization method. The crystallinity and geometry of the as-synthesized nanostructure are confirmed from the X-ray diffraction study, while the chemical architecture of the nanomaterial is ascertained through X-ray photoelectron spectroscopy. The stacking of flat graphene-like layers with a size range of ∼200-300 nm is visualized through high-resolution transmission electron microscopy images. Further, the as-synthesized carbon nanomaterial is advanced as an effective and highly sensitive electrochemical biosensor for dopamine, a vital neurotransmitter of the human brain. Nanomaterial oxidizes dopamine at a much lower potential (0.13 V) than other metal-based nanocomposites. Moreover, the obtained sensitivity (13.75 and 3.31 μA μM
MeSH term(s)	Humans ; Carbon ; Dopamine/chemistry ; Reproducibility of Results ; Electrochemical Techniques/methods ; Metals ; Plant Extracts
Chemical Substances	Carbon (7440-44-0) ; Dopamine (VTD58H1Z2X) ; Metals ; Plant Extracts
Language	English
Publishing date	2023-05-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2023.116151
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