MRI and SPECT Brain Image Analysis Using Image Fusion

Kalpana, V.; Kishore, V. Vijaya; Satyanarayana, R.V.S.

doi:10.1007/978-981-19-7982-8_48

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…To correctly discriminate between benign and malignant skin lesions, Ref. 23 created a deep CNN model. They used transfer learning to fit this model into another model.…”

Section: Literature Surveymentioning

confidence: 99%

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Ali,

Manikandan,

Lei

et al. 2024

Sci Rep

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Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

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“…To correctly discriminate between benign and malignant skin lesions, Ref. 23 created a deep CNN model. They used transfer learning to fit this model into another model.…”

Section: Literature Surveymentioning

confidence: 99%

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Ali,

Manikandan,

Lei

et al. 2024

Sci Rep

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“…Security is one of the primary objectives of information transmission over a network. Therefore, multimedia information security has become critical in e-health, the military, e-commerce, banking transactions, and mobile applications [1,2]. Communication information (plain text) must be protected from unauthorized users to provide security properties to multimedia content.…”

Section: Introductionmentioning

confidence: 99%

An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network

Kumar,

Revathi

2024

J. Eng. Appl. Sci.

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In the last few years, multimedia technology has made tremendous strides. These days, the Web is frequently used to transfer multimedia content, including audio, video, and photos. However, the Internet is a very vulnerable medium with many security holes. To ensure that multimedia content carried across unprotected channels, like the Internet, is secure and private, several encryption techniques have been proposed. New encryption strategies must be developed because multimedia data streams cannot be encrypted using traditional methods. Therefore, the main goal of the recommended system is to present an analytical research approach for introducing a sophisticated framework wherein the suggested encryption technologies' efficacy is increased through the use of deep neural networks (DNNs). The robustness of the DNN principle is coupled with a discrete memristor-based logistic chaotic map notion for enhanced security performance. In this paper, three distinct encryption algorithms—Arnie cat with an artificial neural network (ANN), Henon map with an ANN, and logistic map with a DNN—are compared for security and performance with the suggested algorithm. Correlation coefficients, information entropy, number of pixels changing rate (NPCR), encryption quality, and encryption duration are the cryptographic analysis parameters examined here. The results show that the recommended implementation enhances security performance without degrading image quality. The proposed algorithm achieves 35.9% of UACI, 99.95% of NPCR, and 7.997231 of entropy.

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A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm

Senbagavalli,

Karpagalakshmi,

Sumathi

et al. 2024

Lecture Notes in Networks and Systems

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MRI and SPECT Brain Image Analysis Using Image Fusion

Cited by 9 publications

References 24 publications

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network

A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm

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