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Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. Image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone Image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods.

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Bone Tumor Detection and Classification Using GoogleNet Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • N. Dharmaraj,
  • A. Suresh Kumar,
  • J. Lakshmikanth,
  • R. T. Charulatha,
  • R. Murugasami,
  • M. A. Amarnath

摘要

Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. Image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone Image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods.