Development of Deep Learning Algorithm for Brain Tumor Segmentation
MS. Jyoti Patil1, G.Pradeepini2

1Ms.Jyoti Patil, Ph. D. Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P. India.
2Dr.G. Pradeepini, Professor, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P. India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2800-2803 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9784109119/2019©BEIESP | DOI: 10.35940/ijeat.A9784.109119
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Abstract: Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errors which can lead too fa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor Deep C Net”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.
Keywords: Cnn, brain tumor, segmentation, tumor levels, deep learning, post-surgical analysis, feature extraction