Lung cancer segmentation and detection using KMP algorithm

Abstract

Author(s): A. Surendar*, Saravanakumar Veerappan, Shaik Sadulla and N. Arvinth

Lung cancer is one of the most common and deadliest forms of cancer globally. In recent years, there has been a growing interest in developing and improving early-stage detection and segmentation algorithms to aid in the timely diagnosis and treatment of this disease. Early detection is crucial for improving patient outcomes, and the use of advanced algorithms such as the KMP (Knuth–Morris–Pratt) algorithm shows promise in this area. In this study, we aim to explore the potential of the KMP algorithm for the segmentation and detection of lung cancer in its early stages. By leveraging this algorithm, we aim to improve the accuracy and efficiency of detecting cancerous regions within lung images, ultimately leading to earlier intervention and improved patient survival rates. To accomplish this, we will utilize a dataset of lung images collected during the first half of 2015. The KMP algorithm, known for its efficiency in string matching, will be adapted to analyze the patterns and features present in these lung images. The goal is to accurately identify and segment cancerous regions within the lung images, enabling early detection and intervention. We will compare the performance of the KMP algorithm with other existing algorithms commonly used in lung cancer detection and segmentation. The results of this study will contribute to the advancement of early-stage lung cancer detection and segmentation techniques, potentially leading to improved patient outcomes and survival rates.

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