International Journal of Interpreting Enigma Engineers (IJIEE)

Advancing Brain Tumor Diagnosis Using Deep Learning and Automated Imaging Techniques

2025-03-24T05:22:26+00:00

The study of tumor detection in brain is aimed for improvement of the required treatment for people that are suffering from brain tumor (BT). Brain tumors are aberrant cell evolutions in the brain, while cancer is an acronym employing to indicate tumors that are cancerous called malignant. CT or MRI investigations are used often to identify malignant regions in the brain. PET, cerebral arteriogram (CA), lumbar puncture (LP), and molecular testing (MT) can also be used to detect BTs. MRI scans are largely used in this investigation to examine the illness condition. The objectives of this study are to detect aberrant images and segment the tumor territory. The segmented mask can measure tumor density for therapeutic purposes.

Diabetic Retinopathy Detection using Federated Learning and Vision Transformers

2025-03-24T06:05:40+00:00

Early detection of Diabetic retinopathy (DR) is essential for preventing blindness because this disease currently stands as the primary cause of blindness. The implementation of traditional deep learning models becomes difficult because data privacy risks alongside insufficient dataset availability among different healthcare institutions. We introduce an FL-ViT cooperative model training framework which supports distributed information processing without requiring shared clinical data ownership. The use of ViTs in self-attention feature extraction from retinal images together with FL technology guarantees privacy standards compliance. Healthcare operators use a Model designed for the sector to produce diagnostic documents that speed up clinical operations. The system achieves 93 percentage accuracy, ROC curve is 0.89 , precision and recall and confusion matrix in DR grading according to APTOS dataset evaluations. The solution resolves issues pertaining to AI scalability and generalizability along with compliance with healthcare ethical requirements.

Deep Learning-Based Detection Systems for Autonomous Vehicles in Challenging Weather Conditions

2025-03-24T07:32:37+00:00

Weather detection systems (WDS) are essential in enhancing decision-making for autonomous vehicles, specifically under challenging and adverse weather conditions. Autonomous systems can effectively classify outdoor weather scenarios using deep learning (DL) techniques, allowing seamless adaptation to dynamic environmental changes. This study introduces a robust DL-driven framework to classify diverse weather conditions and aid autonomous vehicle navigation in typical and extreme scenarios. The proposed framework utilizes advanced transfer learning methods alongside a high-performance Nvidia GPU to evaluate the efficiency of three convolutional neural networks (CNNs): MobileNetV2, DenseNet121, and VGG-16. The experiments were conducted using two comprehensive weather imaging datasets, DAWN2020 and MCWRD2018, combined to classify six distinct weather categories: cloudy, rainy, snowy, sandy, sunny, and sunrise. Experimental outcomes showed outstanding performance for all models, with the MobileNetV2-based system performing the highest detection accuracy, precision, and sensitivity of 97.92%, 97.88%, and 97.95%, respectively. Furthermore, the framework achieved a rapid inference time, with an average processing speed of 7 milliseconds per inference using the GPU. Comparative analysis with existing models of the effectiveness of the presented approach showcases advancements in classification accuracy by a margin of 0.3% to 19.8%. These outcomes provide the framework's practicality classification to facilitate reliable decision-making for autonomous vehicles in diverse conditions.

Reliable Lung Nodule Image Classification Using Texture Based Convolutional Neural Network

2025-03-24T09:27:11+00:00

Lung nodules are important markers of lung cancer, and prompt treatment makes early discovery much more likely to improve patient survival. Computer-Aided Diagnosis (CAD) systems were developed because radiologists find it difficult and time-consuming to classify malignant nodules in Computed Tomography (CT) images. Deep learning developments have consistently enhanced CAD's ability to screen for lung cancer. To improve the accuracy of pulmonary nodule classification, we use a Transferable Texture-Based Convolutional Neural Network (CNN) in this work. In order to maximize feature representation, our model integrates an Energy Layer (EL) to extract texture-based features from the convolutional layer. To guarantee strong classification performance, the suggested method is assessed using important performance metrics as accuracy, sensitivity, specificity, F1-score, and AUC-ROC.

Understanding Defect Detection in Solar Cells: Challenges and Deep Learning Approaches with Electroluminescence Imaging

2025-03-24T10:13:29+00:00

Solar energy has gained considerable attention in recent years due to the pressing need to minimize carbon emissions and fight climate change. Photovoltaic (PV) solar cells are crucial in the harvesting of solar energy, but their efficiency and longevity potential is subjected to the effects of defects (e.g., microcracks, finger interruptions). Identifying and diagnosing these defects is crucial for keeping systems running in optimal performance. Although current systems can achieve good performance using Convolutional Neural Networks (CNNs) with encoder-decoder structures, they do not perform well when modelling long-range dependencies, e.g., in situations with complex backgrounds or ambiguous pseudo-defects. Transformers are good at capturing global dependencies at the cost of losing fine-grained local structural information. In detail, this paper presents a hybrid model of CNNs and transformers to eliminate the limitations of existing SSL techniques by leveraging the strength of EfficientNet and transformers architectures. This design integrates local and global features to enhance defect detection capabilities, offering a robust solution for improving the performance and reliability of photovoltaic systems.