Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques.

BACKGROUND

As the mortality rate of lung cancer is enormously high, its impact is also extremely higher than the other types of cancer. Lung malignancy is thus considered one of the deadliest diseases with a high death rate in the world. It is reported that nearly 1.2 million people are diagnosed with this disease and about 1.1 million individuals are died due to this type of cancer every year. The early detection of this disease is the only solution for minimizing the death rate or maximizing the survival rate. However, the timely identification of lung malignant growth is a complex process and hence various imaging algorithms are employed in the process of detecting lung cancer on time.

AIM

The Computer-Aided Diagnosis (CAD) is highly beneficial for the radiologist to rapidly detect and diagnose the irregularities in advance. The CAD systems usually focus on identifying and detecting the lung nodules. As the treatment of this disease is provided on the basis of its stages, the early detection of cancer has to be given much importance. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer.

OBJECTIVE

The major aim of this work is to categorize the lung nodules from the CT image and classify the tumorous cells for identifying the exact position of cancer with higher sensitivity, precision, and accuracy than other strategies.

METHODS

The methods employed in this study are listed as follows: (i) For the process of de-noising and edge sharpening of lung image, the curvelet transform was used. (ii) The Fuzzy thresholding technique was used to perform lung image binarization and lung boundary corrections. (iii) Segmentation was performed by implementing the K-means algorithm. (iv) By using Convolutional Neural Network (CNN), different stages of lung nodules, like benign and malignant, were identified.

RESULTS

The proposed classifier achieves optimal accuracy of 97.3%, a sensitivity of 98.6% and a specificity of 96.1% which are significantly better than the other approaches. Thus, the proposed approach is highly helpful in detecting lung cancer in its early stages.

CONCLUSION

The results validate that the proposed algorithms are highly capable of classifying the lung images into various stages, which effectively helps the radiologist in the decision-making process.