Lung Cancer Detection using Deep Learning Approach CNN

Thoracic radiography (chest X-ray) is a low-cost scientific imaging approach that is quite successful. However, because to a scarcity of skilled radiologists, the technique’s utility is severely limited. Even recent Deep Learning-based solutions sometimes require a lot of supervision to educate such systems, such as annotated bounding boxes, which is difficult to harvest on a large scale. This study recommends that frontal thoracic X-rays be classified and forecasted using a modified model called MobileNet V2. Every year, Computed Tomography (CT) should save a huge number of lives by finding most tumors early on. However, radiologists confront a significant task in analyzing many these images, and they frequently suffer from observer fatigue, which can affect their performance. As a result, it is necessary to read, identify, and consider CT images quickly. Using the NIH Chest-Xray-14 database, the overall performance of this technique is compared to the current modern-day pathology classification algorithms. Inconsistencies in classifiers were originally investigated using the Area Under the receiver operating characteristic Curve (AUC) data. Overall, the obtained result has a wide range, with an AUC of 0.811 and an accuracy of more than 90%. It is concluded that resampling the dataset improves the model’s performance significantly. The goal is to design a model that could be taught, as well as modified units that used less computational energy and could be used in smaller IoT devices.