An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows

Saha, Dip Kumar

doi:10.1016/j.heliyon.2024.e34242

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e34242

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An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows

Dip Kumar Saha

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2024

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Cited by 3 publications

References 16 publications

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Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

Saha,

Hossain,

Safran

et al. 2024

Sci Rep

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The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression. This study propose an ensemble of hybrid model-based techniques that aims to build a strong detection model that considers many psychological and sociodemographic characteristics of an individual to detect whether a person is depressed. Support vector machines (SVM) and multilayer perceptrons (MLP) are the two fundamental methods used to construct the suggested ensemble approach. The hybrid DeprMVM served as a meta-learner. In this study, the hybrid DeprMVM is a level-1 learner, whereas the SVM and MLP networks are level-0 learners. After the classifiers are trained and tested at level 0, their outputs are based on both the independent and dependent variables in the new data set that was used to train the meta-classifier. The training data class imbalance was reduced by applying the synthetic minority oversampling technique (SMOTE) and cluster sampling together, which improved the accuracy for detecting depression. Additionally, it can effectively reduce the risk of over-fitting from simply duplicating data points. To further confirm the effectiveness of the proposed method, various performance evaluation metrics were calculated and compared with previous studies conducted on this specific dataset. In conclusion, among all the techniques for identifying depression, the suggested ensemble approach had the best accuracy, at 99.39%, and an F1-score of 99.51%.

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Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

Saha,

Hossain,

Safran

et al. 2024

Sci Rep

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Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model

Mujahid,

Khurshaid,

Safran

et al. 2024

BMC Infect Dis

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Lumpy skin disease virus (LSDV) is an extremely infectious, viral, and chronic skin disease that is caused by the Capripox virus. This viral disease is predominantly found in cows. Mosquitoes and ticks are the primary transmitters for the spread of this virus. Recently, LSDV has been rapidly spreading all over the world, especially in several areas of Pakistan, India, and Iran. Thousands of cows have died due to this infectious virus in Pakistan and early detection of LSDV is needed to avoid further loss. The prediction and classification of LSDV are hindered by the lack of publicly available datasets. Despite a few studies using LSDV datasets, such datasets are often small, which may lead to model overfitting. In this regard, we collect the dataset from several online sources, as well as, collecting images from veterinary farms in different areas of Pakistan. Deep learning has been widely used in the medical field for disease detection and classification. Therefore, this study leverages DenseNet deep learning models for LSDV detection and classification. Experiments are performed using VGG-16, ResNet-50, MobileNet-V2, custom-designed convolutional neural network, and Inception-V3. The DenseNet architecture presents a Convolutional Block Attention Module (CBAM) and Spatial Attention (SA) for the prediction and classification of LSD. Results demonstrate that a 99.11% accuracy can be obtained on the augmented dataset while a 94.23% accuracy can be achieved with the original dataset for chicken pox, monkey pox, and LSDV. Comparison with state-of-the-art studies corroborates the superior performance of the proposed model.

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Segmentation for mammography classification utilizing deep convolutional neural network

Kumar Saha,

Hossain,

Safran

et al. 2024

BMC Med Imaging

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Background Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed. Methods Recently, the transformer model has emerged as a method for overcoming the constraints of convolutional neural networks (CNN). Thus, our primary goal was to determine how well an improved transformer model could distinguish between benign and malignant breast tissues. In this instance, we drew on the Mendeley data repository’s INbreast dataset, which includes benign and malignant breast types. Additionally, the segmentation anything model (SAM) method was used to generate the optimized cutoff for region of interest (ROI) extraction from all mammograms. We implemented a successful architecture modification at the bottom layer of a pyramid transformer (PTr) to identify BC from mammography images. Results The proposed PTr model using a transfer learning (TL) approach with a segmentation technique achieved the best accuracy of 99.96% for binary classifications with an area under the curve (AUC) score of 99.98%, respectively. We also compared the performance of the proposed model with other transformer model vision transformers (ViT) and DL models, MobileNetV3 and EfficientNetB7, respectively. Conclusions In this study, a modified transformer model is proposed for BC prediction and mammography image classification using segmentation approaches. Data segmentation techniques accurately identify the regions affected by BC. Finally, the proposed transformer model accurately classified benign and malignant breast tissues, which is vital for radiologists to guide future treatment.

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An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows

Cited by 3 publications

References 16 publications

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model

Segmentation for mammography classification utilizing deep convolutional neural network

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