Aggregated model for tumor identification and 3D reconstruction of lung using CT-Scan

This paper facilitates radiologists in diagnosis of lung tumor and provides with a probability to differentiate between the types of tumor through automated analysis and increase in accuracy. The system is aggregated model for tumor identification and 3D reconstruction of lung using (computed Tomography) CT-scan images in Digital Imaging and Communications in Medicine (DICOM) format to identify the lung tumor (Benign or Malignant) using learning algorithm. The proposed system is capable to reconstruct the 3D model of lung tumor using CT-scan medical images and identify tumor (Benign or Malignant) including location of tumor (Attached to wall or parenchyma) with significant accuracy. The proposed diagnostic software provides significant results with bright CT scans to identify lungs tissue with different orientations by rotating it and reduces the enormous false positive rate by increasing the efficiency and accuracy of the diagnostic procedure. Whereas, CT-scan image is below required brightness or if CT-scan is done in a dark room than the module does not shows considerable results of segmentation. The proposed computer aided diagnosis can help the radiologists to detect tumor at early stage, decrease the enormous false positive rate, and the overall cost of the diagnostic procedure; thus, bringing windfall benefits in the field of medical imaging.