Vimal K Shrivastava1, Narendra D Londhe2, Rajendra S Sonawane3, Jasjit S Suri4. 1. School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India. Electronic address: vimal.shrivastavafet@kiit.ac.in. 2. Electrical Engineering Department, National Institute of Technology, Raipur, India; Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA. Electronic address: nlondhe.ele@nitrr.ac.in. 3. Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. Electronic address: drrajss@gmail.com. 4. Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA; Electrical Engineering Department, Idaho State University (Aff.), Pocatello, ID, USA. Electronic address: jsuri@comcast.net.
Abstract
BACKGROUND AND OBJECTIVE: The need for characterization of psoriasis lesion severity is clinically valuable and vital for dermatologists since it provides a reliable and precise decision on risk assessment. The automated delineation of lesion is a prerequisite prior to characterization, which is challenging itself. Thus, this paper has two major objectives: (a) design of a segmentation system which can model by learning the lesion characteristics and this is posed as a Bayesian model; (b) develop a psoriasis risk assessment system (pRAS) by crisscrossing the blocks which drives the fundamental machine learning paradigm. METHODS: The segmentation system uses the knowledge derived by the experts along with the features reflected by the lesions to build a Bayesian framework that helps to classify each pixel of the image into lesion vs. BACKGROUND: Since this lesion has several stages and grades, hence the system undergoes the risk assessment to classify into five levels of severity: healthy, mild, moderate, severe and very severe. We build nine kinds of pRAS utilizing different combinations of the key blocks. These nine pRAS systems use three classifiers (Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)) and three feature selection techniques (Principal Component Analysis (PCA), Fisher Discriminant Ratio (FDR) and Mutual Information (MI)). The two major experiments conducted using these nine systems were: (i) selection of best system combination based on classification accuracy and (ii) understanding the reliability of the system. This leads us to computation of key system performance parameters such as: feature retaining power, aggregated feature effect and reliability index besides conventional attributes like accuracy, sensitivity, specificity. RESULTS: Using the database used in this study consisted of 670 psoriasis images, the combination of SVM and FDR was revealed as the optimal pRAS system and yielded a classification accuracy of 99.84% using cross-validation protocol. Further, SVM-FDR system provides the reliability of 99.99% using cross-validation protocol. CONCLUSIONS: The study demonstrates a fully novel model of segmentation embedded with risk assessment. Among all nine systems, SVM-FDR produced best results. Further, we validated our pRAS system with automatic segmented lesions against manually segmented lesions showing comparable performance.
BACKGROUND AND OBJECTIVE: The need for characterization of psoriasis lesion severity is clinically valuable and vital for dermatologists since it provides a reliable and precise decision on risk assessment. The automated delineation of lesion is a prerequisite prior to characterization, which is challenging itself. Thus, this paper has two major objectives: (a) design of a segmentation system which can model by learning the lesion characteristics and this is posed as a Bayesian model; (b) develop a psoriasis risk assessment system (pRAS) by crisscrossing the blocks which drives the fundamental machine learning paradigm. METHODS: The segmentation system uses the knowledge derived by the experts along with the features reflected by the lesions to build a Bayesian framework that helps to classify each pixel of the image into lesion vs. BACKGROUND: Since this lesion has several stages and grades, hence the system undergoes the risk assessment to classify into five levels of severity: healthy, mild, moderate, severe and very severe. We build nine kinds of pRAS utilizing different combinations of the key blocks. These nine pRAS systems use three classifiers (Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)) and three feature selection techniques (Principal Component Analysis (PCA), Fisher Discriminant Ratio (FDR) and Mutual Information (MI)). The two major experiments conducted using these nine systems were: (i) selection of best system combination based on classification accuracy and (ii) understanding the reliability of the system. This leads us to computation of key system performance parameters such as: feature retaining power, aggregated feature effect and reliability index besides conventional attributes like accuracy, sensitivity, specificity. RESULTS: Using the database used in this study consisted of 670 psoriasis images, the combination of SVM and FDR was revealed as the optimal pRAS system and yielded a classification accuracy of 99.84% using cross-validation protocol. Further, SVM-FDR system provides the reliability of 99.99% using cross-validation protocol. CONCLUSIONS: The study demonstrates a fully novel model of segmentation embedded with risk assessment. Among all nine systems, SVM-FDR produced best results. Further, we validated our pRAS system with automatic segmented lesions against manually segmented lesions showing comparable performance.
Authors: Ankush Jamthikar; Deep Gupta; Narendra N Khanna; Tadashi Araki; Luca Saba; Andrew Nicolaides; Aditya Sharma; Tomaz Omerzu; Harman S Suri; Ajay Gupta; Sophie Mavrogeni; Monika Turk; John R Laird; Athanasios Protogerou; Petros P Sfikakis; George D Kitas; Vijay Viswanathan; Gyan Pareek; Martin Miner; Jasjit S Suri Journal: Curr Atheroscler Rep Date: 2019-05-01 Impact factor: 5.113
Authors: Jasjit S Suri; Sushant Agarwal; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro S C Danna; Luca Saba; Armin Mehmedović; Gavino Faa; Inder M Singh; Monika Turk; Paramjit S Chadha; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanasios D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Pudukode R Krishnan; Ferenc Nagy; Zoltan Ruzsa; Mostafa M Fouda; Subbaram Naidu; Klaudija Viskovic; Mannudeep K Kalra Journal: Diagnostics (Basel) Date: 2022-06-16
Authors: Ankush Jamthikar; Deep Gupta; Narendra N Khanna; Luca Saba; Tadashi Araki; Klaudija Viskovic; Harman S Suri; Ajay Gupta; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Vijay Viswanathan; Andrew Nicolaides; Deepak L Bhatt; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2019-10