CBI + R: A Fusion Approach to Assist Dermatological Diagnoses

2021 ◽  
pp. 2150005
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Skin Diseases ◽  
Treatment Plan ◽  
Diagnostic System ◽  
Data Repository ◽  
Case Based Reasoning ◽  
Advanced Technologies ◽  
Color Features ◽  
Art Works ◽  
Fusion Approach ◽  
Digital Dermoscopy

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI [Formula: see text] R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.

Decision Support Techniques for Dermatology Using Case-Based Reasoning

2020 ◽  
Vol 20 (03) ◽  
pp. 2050024
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Decision Support ◽  
Skin Diseases ◽  
Treatment Plan ◽  
Empirical Evaluation ◽  
Decision Support Techniques ◽  
Skin Lesions ◽  
Data Repository ◽  
Case Based Reasoning ◽  
Hybrid Architecture ◽  
Case Based

Identification of skin disease has become a challenging task with the origination of various skin diseases. This paper presents a case-based reasoning (CBR) decision support system to enhance dermatological diagnosis for rural and remote communities. In this proposed work, an automated way is introduced to deal with the inconsistency problem in CBRs. This new hybrid architecture is to support the diagnosis in multiple skin diseases. The architecture used case-based reasoning terminology facilitates the medical diagnosis. Case based reasoning system retrieves the data which contains symptoms and treatment plan of the disease from the data repository by the way of matching visual contents of the image, such as shape, texture, and color descriptors. The extracted feature vector is fed into a framework to retrieve the data. The results proved using ROC curve that the proposed architecture yields high contribution to the computer-aided diagnosis of skin lesions. In experimental analysis, the system yields a specificity of 95.25% and a sensitivity of 86.77%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works.

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Case-based Reasoning for Skin Diseases Diagnose using Minkowski Distance

2019 ◽  
Author(s):  
. Mihuandayani ◽  
Yufika Sari Bagi ◽  
Theofani Christi Irene Momongan
Keyword(s):  
Skin Diseases ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Case Based

scholarly journals Machine Learning in Medical Imaging for Early Detection of Skin Diseases.

2020 ◽  
Vol 9 (6) ◽  
pp. 513-516
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Early Detection ◽  
Skin Diseases ◽  
Early Stage ◽  
Medical Science ◽  
Diagnostic System ◽  
Skin Disorders ◽  
Medical Field ◽  
Computer Aided

Dermatology is a medical field that treats skin health and diseases. These skin diseases are perilous and often transmittable but can be cured or reversed with higher degree if detected at an early stage. Early detection and treatment can correct most skin disorders. Diagnosis of these diseases requires a sophisticated of proficiency due to the variety of their illustration aspects. As manual conclusion are often skewed and hardly reproducible, to achieve a more intent and undependable diagnosis, a computer aided diagnostic system should be considered. This work is to provide a comparative view of advancements the works as a robust literature of with techniques, methodology, experimented results and dataset done in medical science using medical images to predict diseases with early detection and higher accuracy .

Global Assessment of Functioning

2007 ◽  
Vol 12 (6) ◽  
pp. 1-4, 8 ◽  
Author(s):  
Norma Leclair ◽  
Steven Leclair ◽  
Robert Barth
Keyword(s):  
Mental Disorders ◽  
Global Assessment ◽  
Treatment Plan ◽  
Reliability And Validity ◽  
Diagnostic System ◽  
Veterans Administration ◽  
Global Assessment Of Functioning ◽  
Global Assessment Scale ◽  
Diagnostic And Statistical Manual ◽  
Dsm Iv

Abstract The Global Assessment of Functioning (GAF) is part five of the multiaxial diagnostic system for mental disorders outlined in the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition–Text Revised (DSM-IV-TR). The AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) notes the use of DSM-IV-TR in rating an individual's global functional capacity, which, like disability, is related directly to the effects of impairments. The AMA Guides, Fourth and Fifth Editions, do not provide numeric psychiatric impairment, and shortcomings plague the use of GAF to define disability—but even so, authorities ranging from the State of California to the Veterans Administration rely on GAF scores. A table shows the 100-point scale Global Assessment Scale in which higher scores indicate better functioning. The GAF has been modified to address deficiencies; a decision tree has been added and is summarized; and the editor of DSM-IV-TR has developed a computerized version that reportedly improves reliability and validity. Evaluators should bear in mind that the GAF helps evaluate the individual's functioning in three areas: psychological, social, and occupational (including the activities of daily living). The resulting score facilitates the creation of a treatment plan, evaluates its effectiveness, and predicts outcomes, but evaluators should be aware of its significant limitations.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study

2021 ◽  
pp. 2050074
Author(s):  
John Thomas ◽  
Prasanth Thangavel ◽  
Wei Yan Peh ◽  
Jin Jing ◽  
Rajamanickam Yuvaraj ◽  
...  
Keyword(s):  
Template Matching ◽  
Treatment Plan ◽  
Spectral Feature ◽  
Diagnostic System ◽  
Primary Diagnosis ◽  
Visual Evaluation ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
The Usa ◽  
Scalp Electroencephalogram

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

scholarly journals DESIGN AND IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED HEART DISEASE DIAGNOSIS SYSTEM

2021 ◽  
Vol 3 (1) ◽  
pp. 033-040
Author(s):  
I Putu Agus Eka Pratama
Keyword(s):  
Artificial Intelligence ◽  
Heart Disease ◽  
Design Science ◽  
Diagnostic System ◽  
Science Research ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Design And Implementation ◽  
Heart Disease Diagnosis

As one of the deadliest diseases in the world, heart disease requires serious treatment. The weaknesses of providing services for heart disease in Bali Province are that there is no online diagnostic system to make it easier for people to check their health conditions to find out whether they have heart disease. Based on this research, the design and implementation of a web-based online heart disease diagnosis system are carried out. The diagnostic system uses Artificial Intelligence and inputs data from the user based on several questions posed by the system. This research uses Case-Based Reasoning (CBR) algorithm with Design Science Research Methodology (DSRM) and a case study qualitative research method. The test results show that the system designed and implemented can run well and perform accurate diagnostics according to the design and user needs.

scholarly journals A Little Bit of Dalba Goes a Long Way: Dalbavancin Use in a Vulnerable Patient Population

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S336-S337 ◽  
Author(s):  
Chloe Bryson-Cahn ◽  
Alison Beieler ◽  
Jeannie Chan ◽  
Steve Senter ◽  
Robert Harrington ◽  
...  
Keyword(s):  
Clinical Cure ◽  
Medical Center ◽  
Treatment Plan ◽  
Data Repository ◽  
Persons Who Inject Drugs ◽  
Staphylococcal Infections ◽  
Vulnerable Patient ◽  
The Cost ◽  
Lost To Follow Up

Abstract Background Serious staphylococcal infections require prolonged courses of intravenous (IV) antibiotics. Weekly IV dalbavancin is an alternative to more frequent IV antimicrobial dosing for homeless patients or persons who inject drugs (PWID), for whom creating a treatment plan can be challenging. We examined the clinical outcomes in patients who were treated with dalbavancin compared with a similar population treated with alternative antibiotics. Methods We identified 18 patients who received dalbavancin from June 1, 2015 to December 31, 2016 using pharmacy records and 89 patients receiving IV antibiotics for similar infections treated at Harborview Medical Center from January 1, 2015 to May 31, 2015, before dalbavancin was available. Medical records were reviewed, and patient demographics, length of stay (LOS), readmission, and outcomes were abstracted using REDCap, linked to the University of Washington’s Clinical Data Repository. Results Basic demographics in Table 1. The types of infections are in Figure 1. Clinical cure rates were similar between the two groups (Figure 2) although 21% and 28% of the patients were lost to follow-up in the pre and post dalbavancin period. Among the subgroup of PWID, those who received dalbavancin had higher rates of clinical cure (64.7% vs. 29.4%, P = 0.01), a trend toward decreased LOS (11.4 ± 5.8 vs. 20.2 ± 15.1 days, P = 0.04), and fewer 30-day readmissions (0% vs. 29.4%, P = 0.02) (Figure 2). Fewer PWID in the dalbavancin group were lost to follow-up (23.5% vs. 70.6%). Conclusion Patients treated with dalbavancin had similar outcomes compared with patients treated in the pre-dalbavancin time period. Among PWID, dalbavancin use led to significantly improved outcomes including a higher clinical cure rate, lower readmission rate, and shorter hospital LOS, which offset the cost of the drug. Dalbavancin is an option for the treatment of serious staphylococcal infections in selected patients. Disclosures All authors: No reported disclosures.

ARTIFICIAL INTELLIGENT SYSTEM FOR SKIN DISEASES CLASSIFICATION

2020 ◽  
Vol 32 (05) ◽  
pp. 2050036
Author(s):  
Kamel. K. Mohammed ◽  
Heba M. Afify ◽  
Aboul Ella Hassanien
Keyword(s):  
Feature Selection ◽  
Skin Lesion ◽  
Skin Disease ◽  
Skin Diseases ◽  
Treatment Plan ◽  
Support Vector ◽  
Svm Classifier ◽  
Textural Features ◽  
Artificial Intelligent ◽  
Intelligent Technique

In this paper, an artificial intelligent technique is proposed for skin disease detection and classification. The suggested method comprises four stages, including segmentation, extraction of textural features, and classification. The stretch-based enhanced algorithm has been adapted for image enhancement. Then the method of an active contour is used for segmentation to determine the skin lesion in tissue. Textural features are obtained from the segmented skin lesion. As several numbers of the features can affect the classification precision, ideal feature selection is made to exclude features that are less informative and unnecessary. The feature selection is adjusted with a regularized random forest. Finally, the classification algorithms by support vector machine and a back-propagation neural network (BPNN) are implemented. The dataset consists of 400 dermoscopic images in total divided into 200 benign and 200 malignant skin diseases extracted from the dermoscopic images PH2 database. The result of detecting and classifying the dermoscopic images on these images yielded an accuracy of 99.7%, a sensitivity of 99.4%, and a specificity of 100% by BPNN. The experiential results confirmed that the BPNN classifier is best rather than an SVM classifier for skin disease images. This proposed model will be advanced to support the skin image processing techniques that provided a more accurate diagnosis and rapid treatment plan.

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