Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. In this research work, initially Pre-processing with Chebyshev Filter Combined with Kalman Filtering (HF) approach is done to remove the noise and enhance the image for further processing. Then the preprocessing images undergone Segmentation using Multi-tier Otsu Thresholding (MOT) algorithm. After the process of segmentation, the wavelet and GLCM based feature extraction is executed. Furthermore, introduce a hybrid Attention-based MBConvBlock-EfficientDet (A-MBConvBlock-EfficientDet) model for classification. Specifically, the MBConvBlock is reconstructed to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. Here the Cuckoo Search (CS) Algorithm is used to optimize the A-MBConvBlock-EfficientDet model, potentially enhancing its performance in identifying osteosarcoma lung nodules accurately. The proposed methodology is evaluated through experimental studies. These experiments validate the efficiency of the system in achieving precise osteosarcoma LND. Metrics like accuracy, sensitivity, specificity, and F1 score may be used to assess the performance of the suggested method against existing approaches.
A. Nandhini, M. Sengaliappan
Nehru College of Management, India
Osteosarcoma, Lung Nodule (LN), Cuckoo Search (CS) Algorithm, Attention based EfficientDet and Adaptive Kalman Filter
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 3 , Pages: 3541 - 3551 )
Date of Publication :
February 2025
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