International Journal of Grid and Distributed Computing

Deep Learning Approach to Identify Skin Cancer Diagnosis Using Decoding Predefined

Syed Ali Fathima H. — Sun, 26 May 2024 00:00:00 +0000

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available. Early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. Detecting skin cancer at an early stage is challenging for dermatologists, as well in recent years, both supervised and unsupervised learning tasks have made extensive use of deep learning. Utilizing transfer learning on five cutting-edge convolutional neural networks, both plain and hierarchical classifiers were developed to distinguish seven mole types using the HAM10000 dataset of dermatoscopic images. Incorporating data augmentation techniques, the DenseNet201 network emerged as the most effective, boasting high accuracy and F-measure with minimized false negatives. Interestingly, the plain model outperformed the hierarchical one, particularly in binary classification distinguishing nevi from non-nevi. The research also outlines an extension employing a UNET segmentation model to precisely identify and segment affected areas, aiding doctors in assessing the extent of skin disease.

Enhancing Efficiency And Security Threats In Joint Cloud Storage Data Deduplication

Sreedhar V. — Sun, 26 May 2024 00:00:00 +0000

Cloud storage services have become indispensable in resolving the constraints of local storage and ensuring data accessibility from anywhere at any time. Data deduplication technology is utilized to decrease storage space and bandwidth requirements. This technology has the potential to save up to 90% of space by eliminating redundant data in cloud storage. The secure data sharing in cloud (SeDaSC) protocol is an efficient data-sharing solution supporting secure deduplication. In the SeDaSC protocol, a cryptographic server (CS) encrypts clients’ data on behalf of clients to reduce their computational overhead, but this essentially requires complete trust in the CS. Moreover, the SeDaSC protocol does not consider data deduplication. To address these issues, we propose a secure deduplication protocol based on the SeDaSC protocol that minimizes the computational cost of clients while leveraging trust in the CS. Our protocol enhances data privacy and ensures computational efficiency for clients. Moreover, it dynamically manages client ownership, satisfying forward and backward secrecy.

Efficient Human Heart Disease Detection using Machine Learning Algorithm

M. Sujana — Sun, 26 May 2024 00:00:00 +0000

This project pioneers a novel approach to predicting cardiac disease using Machine Learning algorithms like LR, KNN, SVM, GBC, and the powerful Extreme Gradient Boosting Classifier (XGBoost) with GridSearchCV. Utilizing 5-fold cross-validation, it assesses performance across diverse datasets. The XGBoost Classifier with GridSearchCV achieves outstanding accuracy, hitting 100% in testing and 99.03% in training across multiple datasets, outperforming other algorithms and previous studies. Notably, the XGBoost Classifier without GridSearchCV also demonstrates strong accuracy. Furthermore, an extension employing Random Forest shows comparable accuracy with reduced computation time. This research underscores the efficacy of the proposed technique in advancing cardiac disease prediction.

Advanced Brain Tumor Classification Via Transfer Learning From MRI With Deep Neural Networks

Pondavakam Sukanya — Sun, 26 May 2024 00:00:00 +0000

An innovative approach to brain tumor classification using magnetic resonance imaging (MRI) coupled with deep learning methodologies. By harnessing the power of transfer learning, we employed renowned deep learning architectures like Xception, NasNet Large, DenseNet121, and InceptionResNetV2 to extract intricate features from MRI scans. Through meticulous preprocessing, data augmentation, and training with diverse optimization algorithms on benchmark datasets, our CNN model based on Xception emerged as the most effective, boasting superior accuracy, sensitivity, precision, specificity, and F1-score metrics. This model surpasses existing studies, highlighting its potential for swift and precise brain tumor identification, a pivotal factor in early diagnosis and treatment planning. Additionally, we explored the advanced MobileNetV2 algorithm, achieving a remarkable 100% accuracy when trained on an expansive dataset comprising 3000 images, encompassing both tumor and non-tumor samples.