ANGELIN JEBA J

@annauniversity.edu.in



           

https://researchid.co/angelinjeba

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Computer Networks and Communications, Signal Processing, Computer Vision and Pattern Recognition

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Scopus Publications

Scopus Publications

  • Efficient Skin Cancer Diagnosis and Classification via High-Speed Deep Learning Architecture
    S Rubin Bose, M Izzath Suhail, Shaik Roshni Shabnam, B Hariharan, Angelin Jeba J, and Regin R
    IEEE
    Skin cancer is still a major worldwide health issue, which highlights the necessity for quick and accurate detection methods. This research introduces a robust skin cancer classification system leveraging the capabilities of YOLOv8, An advanced algorithm for object detection. The proposed model processes dermatoscopic images with remarkable speed and accuracy, facilitating the identification of malignant lesions. The YOLOv8 architecture enables classification of various skin cancer types, including melanoma and non-melanoma, by effectively localizing and classifying lesions within the images. A comprehensive dataset, comprising diverse skin lesions, was employed for training and validation, ensuring the model's adaptability to different clinical scenarios. The evaluation of the proposed model demonstrates superior performance compared to traditional methods, exhibiting high sensitivity and specificity.

  • Enhanced Lung Tumor Detection via Deep Learning Techniques in CT Imaging
    S Rubin Bose, V Karrthik Kishore, K R Abishek, J Chalwin Ajay, Angelin Jeba J, and Regin R
    IEEE
    Lung cancer is one of the most common causes of cancer-related death worldwide. Early detection is essential for better patient outcomes. Both the YOLOv8 algorithm and Convolutional Neural Networks (CNNs) have demonstrated promise in the field of medical image processing and object detection, respectively. In this research, we present an innovative method for the detection of lung cancer utilising a CNN model and the YOLOv8 algorithm, integrated into a mobile application developed in Kotlin for enhanced accessibility. Our system takes advantage of the YOLOv8 algorithm's real-time object detection capabilities to recognise nodules of lung cancer using CT scans. The CNN model is trained on a dataset of CT scans and is capable of differentiating between benign and malignant nodules. The mobile application provides a user-friendly interface for uploading CT scans and receiving real-time diagnostic results. The proposed system intends to improve lung cancer early detection by providing a convenient and efficient tool for healthcare professionals and patients. By utilising the strength of CNNs and YOLOv8, this technology has the capacity to reduce false negatives and improve overall diagnostic accuracy, ultimately contributing to better patient care and outcomes. Our model performs well overall, with 98.06% precision, 96.57% recall and 97.31% F1 score when test on a benchmark dataset of images of lung cancer.


  • Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour
    J. Angelin Jeba, S. Nirmala Devi, and M. Meena
    Informa UK Limited

  • Shape Description of FDG uptakes in Pre and Postoperative fused PET/CT Images
    J. Angelin Jeba and S. Nirmala Devi
    IEEE
    Automatic object recognition with shape descriptors help to interact with the real-time environment. This paper describes the shape analysis of radioactivity Fluoro Deoxy Glucose (FDG) uptakes present in pre and postoperative stages of Fused PET/CT images using shape feature extraction approach. Shape features are invariant to various affine transformations such as translation, rotational, flipped, scale, etc., are more robust for shape analysis. Geometrical properties of the FDG uptakes are extracted at various levels of spatial resolution by hierarchical Kdabstraction from the segmented PET/CT images. Rotational invariants considered are the Zernike moments magnitudes and the shape signatures of Fourier descriptors are investigated. The discrimination power of features between pre and post-operative images is examined and evaluated.