Multi-type skin diseases classification using OP-DNN based feature extraction approach

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

Health-related quality of life and its determinants in patients with different dermatological disorders at the University of Gondar Comprehensive Specialized Hospital

OBJECTIVES

Evidence shows that majority of dermatological disorders affect the health-related quality of life (HRQoL) of patients. However, the extent of its negative impact and predictors has not been studied in Ethiopia. Thus, this study looked at assessing the HRQoL and determinants in patients with dermatological disorders (DDs) attending the University of Gondar Comprehensive Specialized Hospital (UoGCSH).

RESULTS

Patients with dermatological disorders (n = 400) were included in the final analysis using a systematic random sampling technique. The mean age of the participants was 39.79 (± 17.17) years. The average (± SD) score of EQ-5D-5 L was 1.92 (± 0.74). Regarding domains, pain/discomfort accounted for a higher proportion 59 (22.3%) followed by anxiety/depression 61 (15.3%). Receiving topical preparations (β = -0.399, 95% CI: -0.6, - 0.19; < 0.001), systemic only medication (β = -0.378, 95% CI: -0.607, -0.149; p = 0.002), having slight, mild, and moderate skin diseases found to have an inverse association with impaired HRQoL, (β = -0.654, 95% CI; -1.01, -0.290); p < 0.001), (β = -0.748, 95% CI: -0.960, -0.538; p < 0.001), and (β = -0.465, 95% CI: -0.642, -0.283; p < 0.001), respectively. Furthermore, age (β = 0.011, 95% CI: 0.006, 0.016; p = 0.001), long duration with skin disease (β = 0.046, 95% CI: 0.015, 0.352; p = 0.013), and presence of comorbidity (β = 0.251, 95% CI: 0.096, 0.402; p = 0.002) were significant predictors of HRQoL among dermatological disease patients.

CONCLUSION

Patients with dermatological disease were found to have a compromised HRQoL. Pain /discomfort problems accounted for a higher proportion compared with other domains. Socio-demographic, clinical and medication-related variables were significantly associated with HRQoL.

Texture classification for visual data using transfer learning

The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because of textures. This work aims to offer texture classification models that will outperform previously presented methods. In this work, transfer learning was applied to attain this goal. MobileNetV3 and InceptionV3 are the two pre-trained models employed. Brodatz, Kylberg, and Outex texture datasets were used to evaluate the models. The models achieved excellent results and achieved the objective in most cases. Classification accuracy obtained for the Kylberg dataset were 100% and 99.89%. For the Brodatz dataset, the classification accuracy obtained was 99.83% and 99.94%. For the Outex datasets, the classification accuracy obtained was 99.48% and 99.48%. The model outputs the corresponding label of the texture of the image.