The main objective of the project is to detect tumor by using Deep Learning Techniques.
In this paper, we proposed a novel technique in order to detect the tumor. Here, we consider the dataset of lungs and pancreas. Spatial transform network is used for the score prediction. Some Deep Learning (DL) methods have been illustrated to reach this goal, including ground truth labeling, semantic segmentation. Use labeled ground truth as training data for machine learning and deep learning models, such as object detectors or semantic segmentation networks. To automate the labeling of ground truth data, you can use a built-in automation algorithm or develop your own algorithm. A semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class. The proposed method results gives better classification results when compared with existing approaches.
Keywords: Tumor, Deep Learning, Semantic Segmentation Networks.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Software: Matlab 2018a or above
Hardware:
Operating Systems:
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB