Chaos based crossover and mutation for securing DICOM image

Image compression techniques: A survey in lossless and lossy algorithms

Brain tumor is the most severe nervous system disorder and causes significant damage to health and leads to death. Glioma was a primary intracranial tumor with the most elevated disease and death rate. One of the most widely used medical imaging techniques for brain tumors is magnetic resonance imaging (MRI), which has turned out the principle diagnosis system for the treatment and analysis of glioma. The brain tumor segmentation and classification process was a complicated task to perform. Several problems could be more effectively and efficiently solved by the swarm intelligence technique. In this paper, the fuzzy brain-storm optimization algorithm for medical image segmentation and classification was proposed, a combination of fuzzy and brain-storm optimization techniques. Brain-storm optimization concentrates on the cluster centers and provides them the highest priority; it might fall in local optima like any other swarm algorithm. The fuzzy perform several iterations to present an optimal network structure, and the brain-storm optimization seems promising and outperforms the other techniques with better results in this analysis. The BRATs 2018 dataset was used, and the proposed FBSO was efficient, robust and mainly reduced the segmentation duration of the optimization algorithm with the accuracy of 93.85%, precision of 94.77%, the sensitivity of 95.77%, and F1 score of 95.42%.

A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images

Multiclass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors. To realize the proposed method, the Densenet201 Pre-Trained Deep Learning Model is fine-tuned and later trained using a deep transfer of imbalanced data learning. The features of the trained model are extracted from the average pool layer, which represents the very deep information of each type of tumor. However, the characteristics of this layer are not sufficient for a precise classification; therefore, two techniques for the selection of features are proposed. The first technique is Entropy–Kurtosis-based High Feature Values (EKbHFV) and the second technique is a modified genetic algorithm (MGA) based on metaheuristics. The selected features of the GA are further refined by the proposed new threshold function. Finally, both EKbHFV and MGA-based features are fused using a non-redundant serial-based approach and classified using a multiclass SVM cubic classifier. For the experimental process, two datasets, including BRATS2018 and BRATS2019, are used without increase and have achieved an accuracy of more than 95%. The precise comparison of the proposed method with other neural nets shows the significance of this work.

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A decision support system for multimodal brain tumor classification using deep learning

The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both ‘weights’ and ‘bias’ parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

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Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system

To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E12(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particul...

The new block pixel sort algorithm for TVC-encrypted medical image

Exchanging a medical image via network from one place to another place or storing a medical image in a particular place in a secure manner has become a challenge. To overwhelm this, secure medical image Lossless Compression LC schemes have been proposed. The original input grayscale medical images are encrypted by Tailored Visual Cryptography Encryption Process TVCE which is a proposed encryption system. To generate these encrypted images, four types of processes are adopted which play a vital role. These processes are Splitting Process, Converting Process, Pixel Process and Merging process. The encrypted medical image is compressed by proposed compression algorithms, i.e Pixel Block Short algorithm PBSA and one conventional Lossless Compression LC algorithm has been adopted JPEG 2000LS. The above two compression methods are used to separate compression for encrypted medical images. And also, decompressions have been done in a separate manner. The encrypted output image which is generated from decompression of the proposed compression algorithm, JPEG 2000LS are decrypted by the Tailored Visual Cryptography Decryption Process TVCD. To decrypt the encrypted grayscale medical images, four types of processes are involved. These processes are Segregation Process, Inverse Pixel Process, 8-Bit into Decimal Conversion Process and Amalgamate Process. However, this paper is focused on the proposed visual cryptography only. From these processes, two original images have been reconstructed which are given by two compression algorithms. Ultimately, two combinations are compared with each other based on the various parameters. These techniques can be implemented in the field for storing and transmitting medical images in a secure manner. The Confidentiality, Integrity and Availability CIA property of a medical image have also been proved by the experimental results. In this paper we have focused on only proposed visual cryptography scheme.

C. Narmatha

Papers

A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images

Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system

The new block pixel sort algorithm for TVC-encrypted medical image

Secure and Efficient Medical Image Transmission by New Tailored Visual Cryptography Scheme with LS Compressions

A Lossless Compression Scheme for Grayscale Medical Images Using a P2-Bit Short Technique