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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS)
Swaroop, Chigurupati Ravi
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Computer Science & IT Engineering Health Professions

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Methodology for eliminating plain regions from captured images Reddy, Shiva Shankar; Gupta, Vuddagiri MNSSVKR.; Srinivas, Lokavarapu V.; Swaroop, Chigurupati Ravi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1358-1370

Abstract

Finding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities.
Evaluation of deep learning models for melanoma image classification Reddy, Shiva Shankar; Rama Raju, Vetukuri Venkata Siva; Swaroop, Chigurupati Ravi; Pilli, Neelima
International Journal of Public Health Science (IJPHS) Vol 12, No 3: September 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i3.22983

Abstract

Melanin-producing cells are the origin of melanoma, the worst form of skin cancer (Melanocytes). If this cancer is not caught early, it might spread to other organs. With automated diagnostic technologies, clinicians and non- professionals may better diagnose diseases. Dermoscopic analysis, biopsy, and histological tests may be needed starting with a clinical assessment. Photo-based skin lesion categorization is challenging due to the fine-grained variability of skin lesions. We provide a more reliable melanoma detection model for each suspicious lesion in this paper. A set of characteristics characterizing a skin lesion's borders, texture, and coloursis used to educate convolutional neural networks. The deep learning models were generated using a standard dataset. To know the model's performance, consider the metrics like accuracy, sensitivity, specificity, Jaccard index and Dice coefficient. Transfer learning is used to categorize normal and diseased skin pictures automatically. This model-driven design helps doctors swiftly assess lesions.
Co-Authors Gupta, Vuddagiri MNSSVKR. Pilli, Neelima Rama Raju, Vetukuri Venkata Siva Reddy, Shiva Shankar Srinivas, Lokavarapu V.