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Dilbag Singh

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4 papers

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JBHI Journal 2026 Journal Article

DARNet: Deep Attention Module and Residual Block-Based Lung and Colon Cancer Diagnosis Network

Manjit Kaur
Dilbag Singh
Ahmad Ali AlZubi
Achyut Shankar
Umashankar Rawat

Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning. To overcome these challenges, we propose an efficient Deep Attention module and a Residual block-based lung and colon cancer classification Network (DARNet). It comprises three key components such as residual blocks, attention modules, and fully connected layers. Residual blocks (RBs) are utilized to refine the DARNet's ability to learn and capture residual information which allows DARNet to perceive complex patterns and improve accuracy. Attention module (AM) enhances feature extraction and captures useful information in the input data. Finally, to achieve better generalization performance, we employ Bayesian Optimization (BO) to fine-tune the hyperparameters of DARNet. Extensive experimental results indicate that the proposed BO-based DARNet achieved superior performance over competitive models on benchmark lung and colon cancer datasets, with a median accuracy of 98. 86% and lower variance.

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JBHI Journal 2025 Journal Article

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution

Dilbag Singh
Ahmad Ali AlZubi
Manjit Kaur
Vijay Kumar
Heung-No Lee

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.

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JBHI Journal 2023 Journal Article

Efficient Evolving Deep Ensemble Medical Image Captioning Network

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

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.

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JBHI Journal 2023 Journal Article

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis

Manjit Kaur
Dilbag Singh
Vijay Kumar
Heung-No Lee

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.

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