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10. Design of a System for Melanoma Diagnosis Using Image Processing and Hybrid Optimization Techniques

Author

Rajinikanth, V., Razmjooy, Navid, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Razmjooy, Navid, editor, Ghadimi, Noradin, editor, and Rajinikanth, Venkatesan, editor

Published
2023
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11. Technical and Economic Evaluation of the Optimal Placement of Fuel Cells in the Distribution System of Petrochemical Industries Based on Improved Firefly Algorithm

Author

Rajinikanth, V., Razmjooy, Navid, Jamshidpour, Ehsan, Ghadimi, Noradin, Dhiman, Gaurav, Razmjooy, Saeid, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Razmjooy, Navid, editor, Ghadimi, Noradin, editor, and Rajinikanth, Venkatesan, editor

Published
2023
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13. A New Home Energy Management System for Smart Home Using Improved Multi-objective Antlion Optimization Algorithm

Author

Razmjooy, Navid, Bahmanyar, Danial, Rajinikanth, V., de Oliveira, Gabriel Gomes, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Iano, Yuzo, editor, Saotome, Osamu, editor, Kemper Vásquez, Guillermo Leopoldo, editor, Cotrim Pezzuto, Claudia, editor, Arthur, Rangel, editor, and Gomes de Oliveira, Gabriel, editor

Published
2023
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16. Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

Author

Rajinikanth, V., Dey, Nilanjan, Raj, Alex Noel Joseph, Hassanien, Aboul Ella, Santosh, K. C., and Raja, N. Sri Madhava

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding; (iii) Image segmentation to extract infected region(s); and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden., Comment: 13 pages

Published
2020

17. Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis

Author

Thanaraj, K. Palani, Parvathavarthini, B., Tanik, U. John, Rajinikanth, V., Kadry, Seifedine, and Kamalanand, K.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN). EEG signal is transformed into an RGB image using Gramian Angular Summation Field (GASF). Many such EEG epochs are transformed into GASF images for the normal and focal EEG signals. Then, some of the widely used Deep Neural Networks for image classification problems are used here to detect the focal GASF images. Three pre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach. Furthermore, the textural features are extracted from GASF images, and prominent features are selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a Custom Convolutional Neural Network (CNN) with three CNN layers, Batch Normalization, Max-pooling layer, and Dense layers, is proposed for epilepsy diagnosis from GASF images. The results of this paper show that the Custom CNN model was able to discriminate against the focal and normal GASF images with an average peak Precision of 0.885, Recall of 0.92, and F1-score of 0.90. Moreover, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper suggests that Deep Learning methods widely used in image classification problems can be an alternative approach for epilepsy detection from EEG signals through GASF images., Comment: 9 pages, 8 Figures, 2 Tables

Published
2020

23. Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

Author

Kadry, Seifedine, Al-Turjman, Fadi, Rajinikanth, V., Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Paiva, Sara, editor, Lopes, Sérgio Ivan, editor, Zitouni, Rafik, editor, Gupta, Nishu, editor, Lopes, Sérgio F., editor, and Yonezawa, Takuro, editor

Published
2021
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24. The Pros and Cons of an Energy-Efficient Q&P Approach to Develop Advanced Steels

Author

Bansal, G. K., Chandan, A. K., Ghosh, Chiradeep, Rajinikanth, V., Srivastava, V. C., Dutta, Monojit, Ghosh Chowdhury, S., Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Bag, Swarup, editor, Paul, Christ Prakash, editor, and Baruah, Mayuri, editor

Published
2021
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25. A Framework to Evaluate and Classify the Clinical-Level EEG Signals with Epilepsy

Author

Chowdhury, Linkon, Chowdhury, Bristy Roy, Rajinikanth, V., Dey, Nilanjan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ray, Kanad, editor, Roy, Krishna Chandra, editor, Toshniwal, Sandeep Kumar, editor, Sharma, Harish, editor, and Bandyopadhyay, Anirban, editor

Published
2021
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26. An Examination System to Classify the Breast Thermal Images into Early/Acute DCIS Class

Author

Dey, Nilanjan, Rajinikanth, V., Hassanien, Aboul Ella, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ray, Kanad, editor, Roy, Krishna Chandra, editor, Toshniwal, Sandeep Kumar, editor, Sharma, Harish, editor, and Bandyopadhyay, Anirban, editor

Published
2021
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27. Brain MRI Examination with Varied Modality Fusion and Chan-Vese Segmentation

Author

Abirami, D., Shalini, N., Rajinikanth, V., Lin, Hong, Rao, Vaddi Seshagiri, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Zhang, Yu-Dong, editor, Bhateja, Vikrant, editor, and Majhi, Ritanjali, editor

Published
2021
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28. Extraction of Cancer Section from 2D Breast MRI Slice Using Brain Strom Optimization

Author

Elanthirayan, R., Sakeenathul Kubra, K., Rajinikanth, V., Sri Madhava Raja, N., Satapathy, Suresh Chandra, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Zhang, Yu-Dong, editor, Bhateja, Vikrant, editor, and Majhi, Ritanjali, editor

Published
2021
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29. Examination of Optic Disc Sections of Fundus Retinal Images—A Study with Rim-One Database

Author

Fuzail Ahmed Razeen, S., Emmanuel, Rajinikanth, V., Tamizharasi, P., Parvatha Varthini, B., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Zhang, Yu-Dong, editor, Bhateja, Vikrant, editor, and Majhi, Ritanjali, editor

Published
2021
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30. Image Assisted Assessment of Cancer Segment from Dermoscopy Images

Author

Santhosh, M., Rubin Silas Raj, R., Rajinikanth, V., Satapathy, Suresh Chandra, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Zhang, Yu-Dong, editor, Bhateja, Vikrant, editor, and Majhi, Ritanjali, editor

Published
2021
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31. Extraction of Leukocyte Section from Digital Microscopy Picture with Image Processing Method

Author

Dellecta Jessy Rashmi, R., Rajinikanth, V., Lin, Hong, Satapathy, Suresh Chandra, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Zhang, Yu-Dong, editor, Bhateja, Vikrant, editor, and Majhi, Ritanjali, editor

Published
2021
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32. Recognition of Skin Diseases Using Curvelet Transforms and Law’s Texture Energy Measures

Author

Chaki, Jyotismita, Dey, Nilanjan, Rajinikanth, V., Ashour, Amira S., Shi, Fuqian, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Balas, Valentina Emilia, editor, Jain, Lakhmi C., editor, Balas, Marius Mircea, editor, and Shahbazova, Shahnaz N., editor

Published
2021
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33. Accurate detection of normal/disease class endoscopic image using DenseNet201 and comparison with VGG16.

Author

Gowtham, K. and Rajinikanth, V.

Subjects
*PYTHON programming language, *DATABASES, *SAMPLE size (Statistics), *DIAGNOSTIC imaging
Abstract

This research seeks to categorise healthy/cancer class endoscopic images using a new pretrained DenseNet201 method, which outperforms pretrained VGG16 with a SoftMax classifier in terms of accuracy (AC). Here are the materials and methods used: In this study, the Kvasir benchmark endoscopy database was used for analysis. Examining images with dimensions of 224×224×3 pixels, the proposed work was carried out using Python software. This study's sample size was determined by setting the following parameters: enrollment ratio=1, confidence interval=95 percent, G Power=80 percent, and threshold=0.05. In this study, we looked at a thousand training photos, with 80% of the data going into training and 20% into certification. This scheme's merit is validated using a 10-fold cross validation, and the received AC is confirmed. In order to identify improvements in AC, this study uses a fixed sample size of N=25 for each category, due to the fact that 25 photos were selected for testing. At last, we will use SPSS, or the Statistical Package for the Social Sciences, to confirm the output (AC). Findings: CLAHE is used to enhance the visibility of the endoscopic pictures under consideration, and searching accuracy, search loss, and confusion matrices are used to confirm the completed classification accuracy. Using SPSS, the suggested approaches provide the following results: Novel DenseNet201 outperforms VGG16 with a mean ACC of 96.6436 percent, while VGG16's mean ACC is 92.9728 percent, with a p-value of 0.001 (p<0.05). The results show that the two groups are significantly different. Based on the results of this study, it is clear that the AC achieved using Novel DenseNet201 is better than the AC achieved using the VGG16 method. Moreover, it is validated that Novel DenseNet201 surpasses VGG16, which is a clinically relevant finding that can be taken into account when analysing actual clinical endoscopic images. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Precise classification of endoscopic pictures using VGG19 and validation against AlexNet.

Author

Gowtham, K. and Rajinikanth, V.

Subjects
*PYTHON programming language, *DATABASES, *CONFIDENCE intervals, *RESEARCH personnel, *SAMPLE size (Statistics)
Abstract

The objective of this research is to categorise endoscopic images as healthy or cancerous using a new pretrained VGG19 strategy that skips connections in order to outperform pretrained AlexNet with a SoftMax classifier in terms of precision (PR). Research Tools and Procedures: The Kvasir benchmark endoscopy database was used for this study's analysis. For the assessment, a picture with dimensions of 224×224×3 pixels was selected (227×227×3 for AlexNet). Python software was used to conduct the proposed work. Using a 0.05 threshold, an 80% G Power, a 95% confidence interval, and a 1 enrollment ratio, the researchers determined the appropriate sample size for the study. In this study, we looked at a thousand training photos, with 80% of the data going into training and 20% into validation. The received PR is confirmed, and the scheme's merit is proven, by a 10-fold cross validation. The study's sample size is set at N=25 for each category in order to identify improvements in AC, since the number of photos for testing is chosen at 25 in this work. At last, the Statistical Package for the Social Sciences (SPSS) will be used to confirm the result (PR). Result: Search accuracy, search loss, and confusion matrices are used to validate the attained classification accuracy after CLAHE processing improves the visibility of the endoscopic images under consideration. Using a p-value of 0.001 (p<0.05), the proposed approaches demonstrate the following results when evaluated with SPSS: VGG19 demonstrates a superior mean PR of 96.7812 percent in comparison to the AlexNet's mean PR value of 92.0668 percent. That there is a notable distinction between the two categories under consideration is proven by this. In the end, the results show that the VGG19 method produced better PR than the AlexNet method. In addition, it has been verified that VGG19 performs better than AlexNet, which is a clinically relevant finding that might be taken into account when analysing actual clinical endoscopic images. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Automatic extraction of nodule in lung CT with watershed technique for improved performance metrics compared with seed region growing method.

Author

Janani, P. N. and Rajinikanth, V.

Subjects
*SEED technology, *COMPUTED tomography, *PULMONARY nodules, *LUNGS, *TEACHING aids, *WATERSHEDS
Abstract

An algorithm for standard segmentation is utilised in this study in order to isolate the nodule area that is present in a CT scan of the lungs. The Watershed Scheme (WS) and Seed Region Growing (SRG) segmentation approaches are utilised in this study in order to calculate the segmentation Accuracy that was achieved (AC). Instructions & Materials Required: The examination of this study centred on the 512×512×1 pixel lung CT slice, and it utilised the LIDC-IDRI benchmark image collection as its means of data collecting. The sample size for this study was selected by applying the following criteria: a threshold of 0.05, a G Power of 80 percent, a confidence interval of 95 percent, and a fixed number of samples (N) of twenty for each class. An analysis of the results will be performed utilising the Novel Watershed and SRG schemes that have been chosen in order to determine ways in which the AC value can be improved. Following that, we will utilise SPSS, which is an acronym that stands for "Statistical Package for the Social Sciences," to establish whether or not the value that was generated is accurate (AC). The results demonstrate that the suggested method enhances AC when applied to the lung CT slices that were analysed, and this improvement is validated when the processed data are compared to the Ground-Truth results (GT). According to the findings of the SPSS analysis of the proposed plan, the following results are displayed: When compared to the SRG value of 81.8050 percent, the AC value of Novel Watershed is significantly higher on the scale of 87.1720 percent. Furthermore, the p-value for this difference is 0.001, indicating that it is statistically significant (p<0.05). As a consequence of this, it is abundantly evident that the two groups in question are quite different from one another. According to the outcome of the study, the investigation reveals that the Novel Watershed approach generates AC that is superior to that produced by the SRG method. Furthermore, it is possible to verify that the automated Novel Watershed scheme is superior to the semiautomatic SRG scheme; this conclusion is clinically important and indicates that there is a need for inquiry into the analysis of genuine clinical lung CT slices. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Implementation of SegNet for precise segmentation of tumor in MRI slice compared to UNet.

Author

Vedhasri, U. and Rajinikanth, V.

Subjects
*CONFIDENCE intervals, *MAGNETIC resonance imaging, *SAMPLE size (Statistics), *ALGORITHMS
Abstract

The objective of this research is to evaluate SegNet and UNet in terms of their capacity to accurately segment tumours in MRI slices and to compare and contrast the two network systems. Materials and Methods of Procedure: For the purpose of this investigation, Flair MRI slices with dimensions of 512x512x3 were obtained from the BraTS2015 comparison image collection. The sample size was determined by using the following parameters: the enrollment ratio was equal to one, the confidence interval was made up of ninety-five percent, the G Power was eighty percent, and the threshold was set at 0.05. Ten test samples are analysed for each research utilising regular segmentation procedures as well as integrated thresholding and segmentation in order to discover which method provides the highest level of accuracy. In the conclusion, verification of the results (accuracy) will be accomplished through the utilisation of the Statistical Package for the Social Sciences (SPSS) programme. Findings: We investigate the process of tumour segmentation using the recently developed SegNet and UNet technologies. When we run the algorithms on ten different samples, we feed the results into SPSS. There is a degree of accuracy of 95.478 percent achieved by both the revolutionary SegNet segmentation and the UNet. P<0.05 causes a significance value of 0.000 to be obtained. The evidence presented here demonstrates that there is a significant difference between the two groups that are being considered. In conclusion, the findings indicate that the cutting-edge SegNet Algorithm performs better than the incumbent UNet algorithm in terms of the accuracy of classification. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Appraisal of Breast Ultrasound Image Using Shannon’s Thresholding and Level-Set Segmentation

Author

Ifan Roy Thanaraj, R., Anand, B., Allen Rahul, J., Rajinikanth, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Himansu, editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, and Li, Kuan-Ching, editor

Published
2020
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39. Assessment of Fundus Images for Retinal Abnormality Screening—A Study

Author

Anita Rose, J. T., Vinnarasi, Sangeetha Francelin, Jesline, Rajinikanth, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Himansu, editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, and Li, Kuan-Ching, editor

Published
2020
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40. An Approach to Extract Low-Grade Tumor from Brain MRI Slice Using Soft-Computing Scheme

Author

Vinnarasi, Sangeetha Francelin, Rose, J. T. Anita, Jesline, Rajinikanth, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Himansu, editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, and Li, Kuan-Ching, editor

Published
2020
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41. Development of a Semiautomated Evaluation Procedure for Dermoscopy Pictures with Hair Segment

Author

Jesline, Rose, J. T. Anita, Vinnarasi, Sangeetha Francelin, Rajinikanth, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Himansu, editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, and Li, Kuan-Ching, editor

Published
2020
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42. Inspection of Crop-Weed Image Database Using Kapur’s Entropy and Spider Monkey Optimization

Author

Rajinikanth, V., Dey, Nilanjan, Satapathy, Suresh Chandra, Kamalanand, K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Kedar Nath, editor, Bansal, Jagdish Chand, editor, Deep, Kusum, editor, Nagar, Atulya K., editor, Pathipooranam, Ponnambalam, editor, and Naidu, Rani Chinnappa, editor

Published
2020
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