8 research outputs found
Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT
No full textA novel approach in multimodality medical image fusion using optimal shearlet and deep learning
No full textReal Time License Plate Recognition Model Using Convolution Neural Network for Intelligent Transportation Systems
No full textOptimal deep learning based image compression technique for data transmission on industrial Internet of things applications
No full textSandpiper Optimization Algorithm With Region Growing Based Robust Retinal Blood Vessel Segmentation Approach
No full textRetinal blood vessel examination is commonly utilized for retinal disease diagnosis by ophthalmologists. The automated retinal vessel segmentation process becomes an essential tool to identify disease. Several retinal vessel segmentation models suffer from a lack of high generalization abilities and low accuracy due to the presence of complex symmetrical and asymmetrical patterns. Robust vessel segmentation of fundus images is needed to improve diagnostic performance including vein occlusion and diabetic retinopathy (DR). In this aspect, this study concentrates on the design of a sandpiper optimization algorithm with region growing based robust retinal blood vessel segmentation (SPORG-RBVS) approach. The proposed SPORG-RBVS technique involves different stages of pre-processing such as grayscale conversion, Z-score-based data normalization, and multi-scale vessel enhancement filtering. The SPO approach addresses the intricate challenges modeled by difficult symmetrical and asymmetrical patterns in retinal vessel segmentation. This method has been specifically designed to improve the generalization capabilities and accuracy of retinal vessel segmentation manners, vital for the precise detection of retinal diseases like vein occlusion and DR. Through phases of preprocessing comprising grayscale conversion, Z-score-based data normalization, and multi-scale vessel enhancement filtering, the SPORG-RBVS model ensures robust segmentation of fundus images. Particularly, the automated segmentation approach employing SPORG incorporates primary seed point generation and threshold determination using the SPO method, contributing to the overall performance of disease detection. A wide-ranging experimental analysis is executed and the outcomes are examined on three benchmark databases such as Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and CHASE_DB1 (CHASE). The comparative study stated the supremacy of the SPORG-RBVS method over existing techniques with maximum accuracy of 98.68%, 98.14%%, and 98.34% under DRIVE, STARE, and CHASE datasets, respectively
Privacy Preservation in Edge Consumer Electronics by Combining Anomaly Detection with Dynamic Attribute-Based Re-Encryption
No full textThe expanding utilization of edge consumer electronic (ECE) components and other innovations allows medical devices to communicate with one another to distribute sensitive clinical information. This information is used by health care authorities, specialists and emergency clinics to offer enhanced medication and help. The security of client data is a major concern, since modification of data by hackers can be life-threatening. Therefore, we have developed a privacy preservation approach to protect the wearable sensor data gathered from wearable medical devices by means of an anomaly detection strategy using artificial intelligence combined with a novel dynamic attribute-based re-encryption (DABRE) method. Anomaly detection is accomplished through a modified artificial neural network (MANN) based on a gray wolf optimization (GWO) technique, where the training speed and classification accuracy are improved. Once the anomaly data are removed, the data are stored in the cloud, secured through the proposed DABRE approach for future use by doctors. Furthermore, in the proposed DABRE method, the biometric attributes, chosen dynamically, are considered for encryption. Moreover, if the user wishes, the data can be modified to be unrecoverable by re-encryption with the true attributes in the cloud. A detailed experimental analysis takes place to verify the superior performance of the proposed method. From the experimental results, it is evident that the proposed GWO–MANN model attained a maximum average detection rate (DR) of 95.818% and an accuracy of 95.092%. In addition, the DABRE method required a minimum average encryption time of 95.63 s and a decryption time of 108.7 s, respectively
Corrections to “Sandpiper Optimization Algorithm With Region Growing Based Robust Retinal Blood Vessel Segmentation Approach”
No full textPresents corrections to the paper, (Corrections to “Sandpiper Optimization Algorithm With Region Growing Based Robust Retinal Blood Vessel Segmentation Approach”)
A Novel Bacterial Foraging Optimization Based Multimodal Medical Image Fusion Approach
Get PDFMultimodal medical image fusion (MIF) is the procedure of integrating different images in single into multiple imaging modalities for increasing the image quality by preserving a certain feature. Medical image combination covered a tremendous count of hot topic areas, involving pattern recognition, image processing, artificial intelligence (AI), computer vision (CV), and machine learning (ML). In addition, MIF was more commonly applied in clinical for physicians to understand the lesion by the combination of various modalities of medicinal image. This article introduces a novel bacterial foraging optimization-based multimodal medical image fusion approach (BFO-M3IFA). The presented BFO-M3IFA technique considered two distinct patterns of the images as the input of systems and the outcome will be the fused image. Primarily, the BFO-M3IFA technique exploits Weiner filtering (WF) technique as an image pre-processing step to get rid of the noise. Besides, discrete wavelet transform (DWT) was applied for decomposing the image into distinct subbands. Afterward, the estimated coefficients of modality 1 and comprehensive coefficients of modality 2 are integrated and vice versa. At last, a fusion rule is generated to fuse the details of two image modalities and the optimal fusion rule parameter is chosen with utilize of BFO algorithm. The experimental validation of the BFO-M3IFA system was tested and outcomes ensured the improved performance of the BFO-M3IFA system on existing models
