Retraction Note to: Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method

Balaji, M. Sundar Prakash; Saravanan, S.; Chandrasekar, M.; Rajkumar, G.; Kamalraj, S.

doi:10.1007/s12652-022-03935-5

Cited by 4 publications

(4 citation statements)

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“…Various research works are held in previous studies for medical image segmentation. Some images are analyzed for this current research function, which is summarized as follows: Recently, different metaheuristic methods have been developed for overcoming the image segmentation issues like Ant Colony Optimization (ACO) [15], Whale Optimization Algorithm (WOA) [16], Particle Swarm Optimization (PSO) [17], Genetic Algorithm (GA) [18], Firefly Optimization (FFO) [19], Cuckoo Search Algorithm (CSA) [20], etc. All the methods presented in the literature have certain restriction for segmentation of images.…”

Section: Related Workmentioning

confidence: 99%

Generative Deep Belief Model for Improved Medical Image Segmentation

Balaji¹

2023

Intelligent Automation &Amp; Soft Computing

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Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform (QCWT). Then, feature extraction and selection are done after the pre-processing of the image. The features are optimally selected by the Golden Eagle Optimization (GEO) method. Specifically, the processed image is segmented accurately using the proposed Generative Heap Belief Network (GHBN) technique. The execution of this research is done on MATLAB software. According to the results of the experiments, the proposed framework is superior to current techniques in terms of segmentation performance with a valid accuracy of 99%, which is comparable to the other methods.

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Section: Related Workmentioning

confidence: 99%

Generative Deep Belief Model for Improved Medical Image Segmentation

Balaji¹

2023

Intelligent Automation &Amp; Soft Computing

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“…Using the energy function defined in Equation ( 27), a probability is assigned to each (visible, hidden) pair. Here, the partition function is represented as Z and it is expressed in Equation (28).…”

Section: E(mentioning

confidence: 99%

“…In 2020, Balaji et al 28 have introduced an optimized NFA for classifying the skin cancer. The fuzzy c‐means segmentation method was used in the proposed work to locate the cancer zone.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent skin cancer diagnosis using adaptive k‐means segmentation and deep learning models

Enturi

Suhasini

Satyala³

2022

Concurrency and Computation

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Summary Since melanoma spreads swiftly throughout the body, it is typically a deadly form of skin cancer. Only when skin cancer is discovered early on is it usually treatable. In order to do this, this work proposes a unique melanoma detection model that has five main phases, including (i) pre‐processing, (ii) segmentation, (iii) feature extraction, (iv) suggested HKPCA based dimensionality reduction, and (v) classification. Pre‐processing is done first, and segmentation is done using a new adaptive k‐means methodology after that. After that, features from the gray‐level co‐occurrence matrix (GLCM), deviation relevance based local binary pattern (DRLBP), and gray‐level run‐length matrix (GLRM) is extracted. Extracted features were subjected for dimensionality reduction via hybrid kernel proposed principal component analysis (HKPCA). These dimension reduced features are then classified using deep belief network (DBN) framework, where the weights will be optimized by means of improved elephant herding optimization (IEHO). Finally, a comparison of the proposed and existing models' convergent performance is conducted.

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“…Steganography gives secret communication by using the carrier of any media and payload of any media as per the requirement of the data to be preserved [1][2][3][4][5]. Imperceptibility, immersing capacity and robustness are the essential and crucial characteristics of a dataconcealing system [6][7][8][9][10][11][12][13][14]. Depending upon the cover media, steganography can be elaborated in various methods, such as image steganography, video steganography, audio steganography and text steganography.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

Patil¹,

Sundari²,

Sivaraman³

2023

Computer Systems Science and Engineering

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The intact data transmission to the authentic user is becoming crucial at every moment in the current era. Steganography; is a technique for concealing the hidden message in any cover media such as image, video; and audio to increase the protection of data. The resilience and imperceptibility are improved by choosing an appropriate embedding position. This paper gives a novel system to immerse the secret information in different videos with different methods. An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user's face image. A hidden message is first included in the audio from the multimedia file; using LSB Technique. The Stego-video is created in the second stage by merging the authorized user's face into the frame of the video; by using PVD technology. Stego-audio is linked again with the stego-video in the third stage. The incorporated perspective techniques (LSB-SS and PVD-SS algorithms) with more significant data immersing capacity, good robustness and imperceptibility are proposed in this research work. The spread spectrum approach is used to increase the complexity of secret data recognition. Two different video files are tested with different voice files with the results such as PSNR, SSIM, RMSE and MSE as 52.3, 0.9963, 0.0024 and 0.0000059, respectively.

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Retraction Note to: Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method

Cited by 4 publications

References 0 publications

Generative Deep Belief Model for Improved Medical Image Segmentation

Generative Deep Belief Model for Improved Medical Image Segmentation

Intelligent skin cancer diagnosis using adaptive k‐means segmentation and deep learning models

Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

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