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Segmentation and detection of soft tissue sarcomas based on mask regional convolutional neural network

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Abstract

Soft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. One of the most successful therapy for patients with this disease is surgical resection. However, it is very challenging to diagnose disease in the early stages and recognize the chemotherapy effect during the chemotherapeutics period due to a lack of radiologists in developing nations. The majority of cancer hospitals require a large number of radiologists, who are highly expensive. The proposed approach develops a Mask-Regional Convolutional Neural Network (Mask-RCNN) for identifying soft tissue sarcomas to solve these problems. Initially, Images are gathered and then pre-processed with image resizing, anisotropic diffusion, as well as contrast stretching. Collected images are in different sizes, so needs to be resized for effective prediction and further improve the quality by using two pre-processing techniques. In order to eliminate the unwanted noise from the original image, anisotropic diffusion is applied. Contrast stretching is used to improve the brightness of the image. In the following step, pre-processed images are sent into Mask R-CNN, which segments and bounds a box around the features. A well-liked deep learning segmentation approach is named as Mask R-CNN that segments objects at the pixel level. These bounded features are classified for detecting whether the disease affected the patients or not. According to the simulation analysis, the proposed approach attains 97% accuracy, 0.03% error, 91% precision, and 95% specificity. Consequently, compared to other existing techniques, the developed approach performs better. This prediction model helps to predict the soft tissue sarcomas at the early stage and improves the patient’s living standard.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of ECE, Chandigarh University, Mohali, Punjab, 140413, India

    Vikas Mittal

  2. Department of CSE, Vidya Academy of Science and Technology Technical Campus, Thiruvananthapuram, 695602, Kerala, India

    Brijilal Ruban

  3. Department of CSE, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India

    Deepika Shekhawat

  4. Department of ETE, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, 411044, India

    Mahesh T. Kolte

  5. School of Commerce and Management, D Y Patil Agriculture & Technical University, Talsande, Maharashtra, 416112, India

    B. Murali Manohar

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  1. Vikas Mittal
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The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-authors provide guidance to verify the analysis result and manuscript editing.

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Correspondence to Vikas Mittal.

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Mittal, V., Ruban, B., Shekhawat, D. et al. Segmentation and detection of soft tissue sarcomas based on mask regional convolutional neural network. Multimed Tools Appl 83, 89195–89215 (2024). https://doi.org/10.1007/s11042-024-19003-3

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  • DOI: https://doi.org/10.1007/s11042-024-19003-3

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