Vol. 8, Special Issue 5 (2019)

Author(s):
RK Tiwari and Sangeeta Yadav

Abstract:
Utilizing cutting-edge technologies like neural networks, machine learning, and image processing has greatly increased the precision and speed of cancer detection. The integration of these technologies with the knowledge of contact inhibition and the characteristics of cancerous cells has enabled the researchers to develop a model that can accurately detect the presence of tumors in patients' lungs and differentiate between benign and malignant tumors. The dataset of CT scan images of patients, along with the application of image enhancement techniques and image segmentation algorithms, allows for the identification and extraction of important features necessary for the accurate detection of tumors. By creating a neural network model, the researchers can obtain the required output for the patient's lung status, which can help medical professionals in their diagnosis and treatment planning. This methodology can significantly reduce the mortality rate associated with lung cancer by enabling timely detection and treatment, which is why it is implemented in the field of medical science. This model can also aid radiologists and medical experts in their evaluation and diagnosis of cancerous cells, providing a more accurate and reliable diagnosis.