Detection of Lung Tumor from MRI scans using Advanced Image Processing Techniques

Electronics and Communication Engineering

The identification of Lung tumors is critical for patients' early diagnosis and therapy planning. Image processing algorithms have developed as useful tools for automatic and reliable tumor diagnosis from medical imaging data in recent years. This research describes a novel approach for detecting Lung tumors using image processing techniques. A series of preprocessing processes are used in the proposed method to improve the quality of Lung pictures and reduce noise. Image scaling, noise reduction, and contrast improvement are all included. Following preprocessing, image segmentation algorithms are used to isolate probable tumor locations and separate the Lung region from the backdrop. Various feature extraction approaches are used to extract significant features from the segmented Lung regions for tumor identification. These criteria capture crucial tumor properties such as form, texture, and intensity fluctuations. The retrieved features are then used to train a classifier to distinguish between tumor and non-tumor regions. Experiments are carried out using a dataset of Lung pictures encompassing both tumor and non-tumor instances to assess the efficacy of the suggested technique. The findings show that the proposed method for detecting Lung tumors is highly accurate and efficient. Comparisons with existing approaches demonstrate the suggested method's advantages in terms of detection accuracy and computing efficiency. Overall, the suggested image-based Lung tumor detection system has significant promise for supporting medical professionals in the early detection of Lung tumors. It has the potential to improve patient outcomes by allowing for timely intervention and personalized treatment regimens.

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