Browsing by Author "Samad, Md Abdus"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques
    (MDPI Publications, 2023-11-01) Islam, Md. Monirul; Alam, K. M. Rafiqul; Uddin, Jia; Ashraf, Imran; Samad, Md Abdus
    Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task.
  • Thumbnail Image
    Item
    Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review
    (MDPI Publications, 2023-11-23) Hossain, Mohammad Mobarak; Kashem, Mohammod Abul; Islam, Md. Monirul; Sahidullah, Md.; Mumu, Sumona Hoque; Uddin, Jia; Aray, Daniel Gavilanes; Diez, Isabel de la Torre; Ashraf, Imran; Samad, Md Abdus
    The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.