Kyi Pyar Zaw’s research while affiliated with University of Technology Yatanarpon Cyber City and other places

This study presents an advanced approach to multi-class skin lesion classification by leveraging an ensemble model comprising the Inception-V3, ResNet-50, and VGG16 architectures. The classification task focuses on categorizing skin lesions into distinct classes, including Melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), using the ISIC dataset, a comprehensive collection of dermoscopic images. In order to properly balance the dataset, the oversampling strategy is utilized, as some lesion types are underrepresented due to inherent imbalances in the dataset. By ensuring that the model is trained on a more representative dataset, this balancing improves the algorithm's capacity to categorize all lesion types properly and impartially. By combining the complementary features of ResNet-50, Inception-V3, and VGG16, the ensemble technique improves the overall classification performance. ResNet-50 is chosen for its deep feature extraction capabilities, which help capture fine details in lesion patterns. Inception-V3 is selected for its multi-scale processing, allowing it to effectively analyze lesions at varying resolutions and sizes. VGG16 is included due to its simple yet highly effective architecture for image classification tasks. The ensemble model with data augmentation significantly outperforms individual models in skin lesion classification for both the original and balanced ISIC datasets regarding accuracy, precision, recall, and F1-score. This method offers a robust solution for skin lesion classification, contributing to more accurate and reliable diagnostic tools in dermatology.

This paper publicizes the character segmentation and recognition of the Myanmar warning text signboard images taken by a mobile phone camera in natural scene. In this system, two templates are created. The first template that contains both connected pixel words and characters are used for character segmentation and the second template that contains only the connected pixel characters are used for character classification. Color enhancement process is first performed to extract the text regions. By preprocessing the color enhancement, the system can overcome the some illumination conditions. To remove the background noises on the binary images, color threshold based filtering, aspect ratio based filtering, boundary based filtering and region area based filtering techniques are used. As a next step, line segmentation and character segmentation are done. Line segmentation is performed using horizontal projection profile and character segmentation is done using vertical projection profile and bounding box methods. In the character segmentation process, template matching method is used by training connected pixel words. These connected component characters are recognized using 4 × 4 blocks based pixel density and total chain codes, four rows-based pixel density, four columns-based pixel density and count of eight directions chain code on the whole character image and on each block of character image. This system is investigated by feature-based approach of template matching on 160 camera-captured Myanmar warning signboards.