39 research outputs found
Author Correction: An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database
No full textRETRACTED: Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images
No full textDiabetic retinopathy (DR) and adult vitelliform macular dystrophy (AVMD) may cause significant vision impairment or blindness. Prompt diagnosis is essential for patient health. Photographic ophthalmoscopy checks retinal health quickly, painlessly, and easily. It is a frequent eye test. Ophthalmoscopy images of these two illnesses are challenging to analyse since early indications are typically absent. We propose a deep learning strategy called ActiveLearn to address these concerns. This approach relies heavily on the ActiveLearn Transformer as its central structure. Furthermore, transfer learning strategies that are able to strengthen the low-level features of the model and data augmentation strategies to balance the data are incorporated owing to the peculiarities of medical pictures, such as their limited quantity and generally rigid structure. On the benchmark dataset, the suggested technique is shown to perform better than state-of-the-art methods in both binary and multiclass accuracy classification tasks with scores of 97.9% and 97.1%, respectively
Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach
No full textAbstract Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists’ expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians
Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique
No full textTo improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
No full textAbstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively
Corn leaf disease diagnosis: enhancing accuracy with resnet152 and grad-cam for explainable AI
No full textAbstract Objective The agricultural sector is important in the supply of food globally as well as in enhancing the economy especially in developing countries where it forms the backbone of the economy. Corn can be classified as such crops important to the world’s food system. Unfortunately, corn crop is vulnerable to a lot of diseases and this may result into heavy losses and disruption in the food supply system. Hence, it is important to detect and classify these diseases accurately and promptly to limit losses and achieve the highest possible productivity. This study intends to solve these problems by constructing a trustworthy and interpretable model based on deep learning approaches focused on accurate identification of corn leaf disease. Material In this study, the cumulative dataset comprises 4188 images which are further divided into four classes of corn leaf disease, with 1146 images of blight leaf, 1306 images of common rust, 574 images of gray spot and 1162 images of healthy leaves. In order to train and validate the model 70% of data was used for training while 30% was used for testing. This division was appropriate because it allowed enough data to be used during model training and also enough for model evaluation on new data. Methods The research employs ResNet152, a well-known deep leaning structure in image classification, because uses residual connections that improve the training of deep networks. Furthermore, Grad-CAM (Gradient-weighted Class Activation Mapping) is employed to improve the explainability of the model. Grad-CAM produces human interpretable visual images in the form of heatmaps and it indicates the areas of corn leaves that have had the greatest impact in the model, which is fairly useful in understanding the model. The model processes and predicts the corn leaves into four classes: healthy (H), blight (B), gray spot (GS) and common rust (CR), with precision and explainability. Results The results of training the ResNet152 model were remarkable as it registered a 99.95% accuracy during training as well as 98.34% during testing. Also, applying Grad-CAM for interpretability purposes proved to be useful as it created heatmaps that indicated the most important parts of the leaf images for making the model predictions. This added to the understanding of the model and its predictions, which was especially important for users such as farmers who required accurate diagnoses of diseases. Conclusion This study demonstrates the effectiveness of the ResNet152 model, enhanced with Grad-CAM for explainability, in classifying corn leaf diseases. Achieving good training and testing accuracy, the model provides transparent, human-readable explanations, fostering trust and reliability in automated disease diagnosis and aiding farmers in making better-informed decisions to improve crop yields
Correction: Srinivasan et al. Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images. Electronics 2023, 12, 862
No full textThere was an error in the original publication [...
An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data
No full textEarly diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them
Secure Healthcare Access Control System (SHACS) for Anomaly Detection and Enhanced Security in Cloud-Based Healthcare Applications
No full textThe growing reliance on distributed cloud technology in mobile healthcare applications has introduced critical challenges in ensuring secure and efficient access to Electronic Health Records (EHR). Traditional methods have prolonged authentication times and access delays, compromising both the efficiency and security of healthcare systems. To address these issues, this study proposes the Secure Healthcare Access Control System (SHACS), a robust framework specifically designed to enhance security and efficiency in healthcare environments. SHACS provides a sophisticated combination of role-based access control, attribute-based policies, and dynamic rules to streamline authentication processes and safeguard data access. SHACS architecture provides the central authority and system authorities, responsible for enforcing access control policies and verifying the authenticity of users requesting access to medical records. SHACS also integrates real-time anomaly detection capabilities, utilizing the MIMIC-III dataset to identify and respond to unusual access patterns, thereby mitigating potential security breaches. Following successful authentication, SHACS generates secure decryption tokens and keys, enabling swift and secure access to EHRs while continuously updating a dynamic access list to monitor and reduce access delays. Experimental results demonstrate that SHACS significantly improves system performance, reducing authentication times by 30% and access delays by 25% compared to traditional methods. For instance, SHACS decreased the average authentication time from 40 seconds to 28 seconds and enhanced system responsiveness, lowering average access delays from 15 seconds to 11 seconds. The implementation of SHACS underscores the importance of privacy-enhancing technologies in safeguarding medical records, ensuring that only authorized personnel access sensitive data. Through rigorous testing and analysis, SHACS proves its efficacy in strengthening the security posture of cloud-based healthcare systems, ultimately contributing to the quality and accessibility of remote healthcare services
Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.
No full textIn the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images. The method combines semantic and spatial data using cGAN framework to produce realistic urban scenes while maintaining crucial details. To assess the performance of the proposed method, extensive experiments are performed on benchmark datasets, the ISPRS Potsdam and Vaihingen datasets. Intersection over Union (IoU) and Pixel Accuracy are two quantitative metrics used to evaluate the segmentation accuracy of the produced maps. The proposed method outperforms traditional methods with an IoU of 87% and a Pixel Accuracy of 93%. The experimental findings show that the suggested cGAN-based method performs better than traditional techniques, attaining better segmentation accuracy and generating better urban maps with finely detailed information. The suggested approach provides a framework for resolving the image-to-image translation difficulties in urban scene segmentation, demonstrating the potential of cGANs for producing excellent urban maps from satellite data
