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All Journal Jurnal Plastik Rekonstruksi Emerging Information Science and Technology
Saphira, Chairani Fitri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Other

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Triple VY Advancement Flap For Vulva And Anal Reconstruction In Extensive Perineal Squamous Cell Carcinoma In-Situ Saphira, Chairani Fitri; Tan, Bien Keem; Tay, Sun Kuie
Jurnal Plastik Rekonstruksi Vol. 4 No. 2 (2017): Jurnal Plastik Rekonstruksi
Publisher : Lingkar Studi Bedah Plastik Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (241.387 KB) | DOI: 10.14228/jpr.v4i2.234

Abstract

Background : Algorithm for vulvar reconstructions in defects after oncologic resection of vulvar tumors has been established. It goals are to restores form and function for the purpose of coitus, micturition, and defecation. The purpose of this article is to present our experience in vulvar and anal reconstruction. Method : A 43-year-old female with extensive vulvar, perineal and anal squamous cell carcinoma in-situ and groin lymph nodes metastasis requiring defect coverage after tumor resection. The defects involving exposed soft tissue around vulvar, perineal and anal areas. Result : Vulvar and anal reconstruction was done in stages utilizing triple VY advancement flap. The reconstruction was successful with preservation of sexual and anal functions; and minimal scarring. Conclution: Extensive vulvar and anal defects can be reconstructed by recruiting local tissue. VY advancement flap is one of the best options in these reconstructions.
Burn Severity Classification Using Deep Learning and Transfer Learning Saphira, Chairani Fitri; Tahalele, Paul L
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.31493

Abstract

The fast and accurate evaluation of the degree of thermal damage still remains a key factor in the decision-making process but the accuracy of diagnosis is limited by the subjectivity of the clinical knowledge. The current study is aimed at the examination of the deep learning approach towards the automated classification of the skin burns into three degrees using CNNs and transfer learning approaches. A carefully curated dataset of 1,221 burn images was prepared using resizing, normalization, and data augmentation. Additionally, the class weighting approach was used in order to overcome the problems associated with the skewness of the data. The comparison of four different architectures was conducted including CNN, MobileNetV2, EfficientNetB0, and ResNet50 using ImageNet pre-trained weights. Experimental results indicate that ResNet50, tuned using the two-stage training approach, performed best, with test accuracy of 81.81%, precision 0.92, recall 0.90, and F1 score 0.91. The custom CNN model was unable to exhibit stochastic convergence, achieving baseline accuracy of 42.57%, whereas MobileNetV2 and EfficientNetB0 delivered moderate results with 50.89% and 61.65%, respectively. Confusion matrix analysis reveals that the residual learning structure generalized well in all levels of severity, including the important minority class of third-degree burns. The results indicate the dominance of deep residual networks in classifying medical images with insufficient data. The results suggest the use of transfer learning in burn assessment support systems.
Co-Authors Paul L Tahalele Tan, Bien Keem Tay, Sun Kuie