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2. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

3. Analysis of Synthetic Data Generation Techniques in Diabetes Prediction

Author

Das, Sujit Kumar, Roy, Pinki, Kumar Mishra, Arnab, 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, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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4. Artificial Intelligence in Radiological COVID-19 Detection: A State-of-the-Art Review

Author

Kumar, Abhishek, Roy, Pinki, Mishra, Arnab Kumar, Das, Sujit Kumar, 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, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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9. ADASemSeg: An Active Learning Based Data Adaptation Strategy for Improving Cross Dataset Breast Tumor Segmentation

Author

Mishra, Arnab Kumar, Roy, Pinki, Bandyopadhyay, Sivaji, Das, Sujit Kumar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Doriya, Rajesh, editor, Soni, Badal, editor, Shukla, Anupam, editor, and Gao, Xiao-Zhi, editor

Published
2023
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13. Pre-trained EfficientNet-B0 with Adjusted Optimizer, Learning Rate and Image Size to Improve Diabetic Foot Ulcers Diagnosis

Author

Das, Sujit Kumar, Roy, Pinki, Mishra, Arnab Kumar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Chong, Peter Han Joo, editor, Kalam, Akhtar, editor, Pascoal, Antonio, editor, and Bera, Manas Kumar, editor

Published
2022
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16. An Image Encryption Method Using Chaos and DNA Encoding

Author

Biswas, Mukul, Das, Sujit Kumar, Dhara, Bibhas Chandra, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Dutta, Paramartha, editor, Mandal, Jyotsna K., editor, and Mukhopadhyay, Somnath, editor

Published
2021
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18. Machine Learning Based Automatic Prediction of Parkinson’s Disease Using Speech Features

Author

Jain, Deepali, Mishra, Arnab Kumar, Das, Sujit Kumar, 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, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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21. A Critical Survey of Mathematical Search Engines

Author

Dhar, Sourish, Roy, Sudipta, Das, Sujit Kumar, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mandal, Jyotsna Kumar, editor, Mukhopadhyay, Somnath, editor, Dutta, Paramartha, editor, and Dasgupta, Kousik, editor

Published
2019
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24. A New Data Hiding Method Using Block Pixel Intensity Range

Author

Das, Sujit Kumar, Dhara, Bibhas 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, Bhattacharyya, Siddhartha, editor, Chaki, Nabendu, editor, Konar, Debanjan, editor, Chakraborty, Udit Kr., editor, and Singh, Chingtham Tejbanta, editor

Published
2018
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30. Diabetic Foot Ulcer Identification: A Review.

Author

Das, Sujit Kumar, Roy, Pinki, Singh, Prabhishek, Diwakar, Manoj, Singh, Vijendra, Maurya, Ankur, Kumar, Sandeep, Kadry, Seifedine, and Kim, Jungeun

Subjects
*DIABETIC foot, *CONVOLUTIONAL neural networks, *COMPUTER vision, *DEEP learning, *AUTOMATIC identification, *FOOT diseases
Abstract

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient's lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal. [ABSTRACT FROM AUTHOR]

Published
2023
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37. CR‐SSL: A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation.

Author

Mishra, Arnab Kumar, Roy, Pinki, Bandyopadhyay, Sivaji, and Das, Sujit Kumar

Subjects
SUPERVISED learning, BREAST ultrasound, BREAST tumors, EARLY detection of cancer
Abstract

Breast ultrasound (BUS) tumor segmentation can help with the early detection of breast cancer; however, the lack of human‐labeled training data is a big problem in this area. To deal with this, we propose a novel self‐supervised learning approach (CR‐SSL), where several related pretext tasks like unsupervised segmentation and edge detection are firstly learned, followed by the target tumor segmentation based fine‐tuning. Learning such related pretext tasks ensures better representation learning. Experimental study shows that CR‐SSL can improve the mean Dice and Jaccard scores by more than 4–5%, with ≥0.6 scores while having access to only 20–50 human‐labeled training samples. The best Dice scores of 0.7303 and 0.8207, and Jaccard scores of 0.7082 and 0.8015 are obtained by CR‐SSL on the test datasets. Compared with the No‐SSL baseline, CR‐SSL can achieve (10–20)% improvements in segmentation quality while working in small‐sized training dataset scenarios, suggesting its high potential practical utility. [ABSTRACT FROM AUTHOR]

Published
2022
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38. Oversample‐select‐tune: A machine learning pipeline for improving diabetes identification.

Author

Das, Sujit Kumar, Roy, Pinki, and Mishra, Arnab Kumar

Subjects
MACHINE learning, FEATURE selection, DIABETES, RANDOM forest algorithms, CHRONIC diseases
Abstract

Summary: Diabetes is one of the most common chronic disease causes severe life threatening complications. Therefore, it is important to diagnose diabetes at early stage to avoid health and financial burdens. In this work, a machine learning (ML) pipeline based systematic data‐driven architecture is proposed to identify diabetes. The proposed ML pipeline consisted of support vector machine‐synthetic minority oversampling technique (SVM‐SMOTE), followed by multiple tree based feature selection (FS) approaches, and ensemble learners. Further, Bayesian optimization (BO) has been used to tune the hyperparameters in classifiers. The use of SVM‐SMOTE, FS, and BO methods together improved classifiers' performance impressively in a highly imbalanced Virginia dataset. Also, the proposed model is proved to be a useful approach in comparatively less imbalanced Pima Indian Diabetes (PID) dataset. Among all classifiers used, random forest (RFC) has achieved the highest sensitivity of 91.44% in PID dataset and in Virginia AdaBoost (ABC) has achieved the highest of 88.53% sensitivity. Subsequently, XGBoost (XGB) and AdaBoost (ABC) classifiers have achieved the highest 92.08% and 88.27% AUC in PID and Virginia dataset, respectively. Such kind of impressive results suggest that the proposed approach can have a very high practical utility, in real medical diagnostic settings. [ABSTRACT FROM AUTHOR]

Published
2022
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39. Fusion of handcrafted and deep convolutional neural network features for effective identification of diabetic foot ulcer.

Author

Das, Sujit Kumar, Roy, Pinki, and Mishra, Arnab Kumar

Subjects
DIABETIC foot, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, HUMAN skin color, FEATURE extraction, FOOT, SUPPORT vector machines, FOOT orthoses
Abstract

Summary: Among all diabetic‐related complications, the diabetic foot ulcer (DFU) is severe, demanding serious attention, and timely treatment. The purpose of the present study was to conduct features fusion of machine learning (ML) based handcrafted low‐level and convolutional neural networks (CNNs) based high‐level features for improving automatic diagnosis of DFU. A standard image dataset containing 1038 abnormal (DFU) and 641 normal color skin patches has been used for experimental evaluation. Handcrafted features are extracted to uncover the edge, color, shape, and texture information from the images. Also, a new CNN architecture has been proposed based on deeper residual blocks for extracting high‐level features. Features fusion with several ML classifiers like logistic regression classifier, support vector machine, gradient boosting, and artificial neural network have shown improved DFU identification results compared to the individual feature categories. Thereby, LRC outperformed the state‐of‐the‐art results for all evaluation metrics by achieving 95.23% sensitivity, 95.37% F1‐score, and 96.50% area under the curve (AUC) value. Such impressive experimental observations suggest that the proposed approach is expected to provide efficient decision support to medical practitioners, thereby improving patient care. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach.

Author

Das, Sujit Kumar, Roy, Pinki, and Mishra, Arnab Kumar

Subjects
*DIABETIC foot, *CONVOLUTIONAL neural networks, *FOOT, *ISCHEMIA, *LEG amputation, *INFECTION
Abstract

Diabetic foot ulcers (DFUs) result in amputation of lower limbs or feet without timely assessment and treatment. The assessment of DFUs is performed by the diagnosis of DFU ischaemia and infection. In this work, a new deep convolutional neural network (CNN) based approach (ResKNet) is proposed to perform such assessment. The proposed network consists of a series of unique residual blocks of 2D convolution, batch normalization, and LeakyReLU with skip connections. It has been experimentally observed that a shallower network with 4 such unique residual blocks (Res4Net) can achieve promising results in ischaemia recognition, with an impressive AUC value of 0.9968. However in case of infection recognition, a network with 7 such residual blocks (Res7Net) achieved the best performance, with an AUC value of 0.8890. Such improvements to the current state‐of‐the‐art results suggests that the proposed approach can provide significant help to medical experts, for automatic assessment of DFU. [ABSTRACT FROM AUTHOR]

Published
2022
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