Intelligent Diagnosis of Lung Cancer Using Deep Learning

Seyedeh Nastaran Bapir, Seyed Enayatallah Alavi, Marjan Naderan Tahan



Introduction: Lung cancer, a highly prevalent disease worldwide, poses a significant risk to individuals. Nodules, which manifest as minuscule masses in the lungs, serve as crucial indicators of the early stages of the disease, with the possibility of being either benign or malignant in nature. Prompt diagnosis of this ailment plays a pivotal role in saving patients' lives, thus rendering the utilization of computed aided diagnosis methods exceedingly valuable within this domain.

Material and Methods: The methodology employed for presentation purposes is deeply rooted in the principles of deep learning, a field that epitomizes the amalgamation of artificial intelligence and neuroscience. Delving into the specifics, the initial phase of this process entails the preprocessing of data, wherein the lung area is meticulously isolated from computed tomography (CT) scan images. Subsequently, in the second stage, the identification of nodules is facilitated through the employment of the mask region convolutional neural network (RCNN) technique, which effectively entails the delineation of masks and bounding boxes. The third and final step involves the classification of the identified nodules, achieved through the utilization of a singular convolutional neural network, ultimately segregating the nodules into three distinct categories: benign, malignant, and ambiguous. In order to evaluate the efficacy of the proposed method, the LIDC-IDRI dataset was employed as a means of testing, thereby furnishing tangible evidence to substantiate the claim that the presented method is on par with its counterparts within the realm of detecting and classifying lung nodules.

Results: It is worth noting that the proposed method has yielded a remarkable accuracy rate of 95% in the phase of nodule detection, further bolstering its credibility and reliability. Furthermore, the accuracy rate achieved during the step of nodule classification stands at an impressive 97.3%, thereby cementing the efficacy of the proposed method in a comprehensive manner.

Conclusion: The purpose of this work to provide an intelligent system for reducing the amount of the workload of the physicians in this field. After examining and studying some data set, the LIDC-IDRI data set is presented, for this work because of being suitable for two works of detecting nodule place and nodule classification, lots of data, and because of being reliable and used in previous reliable works that can provide the ability to compare the results.


Lung Cancer Diagnosis; CT Scan Images; Classification; Nodules; Deep Learning;


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