Computer-Based Segmentation of Cancerous Tissues in Biomedical Images Using Enhanced Deep Learning Model

Abstract

In this manuscript, we proposed an automatic segmentation method which was developed using the depth-wise separable convolution with bottleneck connections. The data were normalized using group normalization for reducing the computational complexities and clipped RELU was used with ceiling capped at 6. The network was trained on the datasets of brain tumor and skin cancer while it was tested on the same as well as different datasets acquired under different environments. Additionally, for the case of the brain tumor, the network was tested on real-time MRI dataset. The quantitative and qualitative analysis of results inferred the superior performance of the proposed network. The mIoU and BF Score were increased by 3% and 4.5% for brain tumor segmentation when the network was tested on the different dataset without retaining. For skin cancer dataset an increment of 3% and 5% was observed in both the evaluation metrics. The results obtained on real-time MRI data of brain tumor showed the improvement of (4.2 ± 0.024)% and (4.6 ± 0.0286)%, respectively, in mIoU and BF score. The proposed model produced accurate boundary and pixel details for medical diagnostic purposes. Experienced radiologists did external validation of the proposed method by comparing the obtained results with the manually segmented images. This computer-assisted approach can save the time and burden of doctors for the diagnosis of cancer.

KEYWORDS:

• Bottleneck connections
• Brain tumor
• Deep learning
• Depth-wise separable convolution
• Image segmentation
• Skin cancer

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Sumit Tripathi

Sumit Tripathi is an assistant professor in the Department of Electronics and Communication Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand. The author has 13 years of academic and research experience. He is lifetime member of the Indian Society of Technical Education (ISTE). His research interest includes artificial intelligence, machine learning, deep learning, and biomedical image processing.

Neeraj Sharma

Neeraj Sharma is a professor in the School of Biomedical Engineering, Indian Institute of Technology, Varanasi. The author is a life member of the Association of Medical Physicist of India. His research interest includes biomedical instrumentation, deep learning, biomedical image, and signal.Email: [email protected]