The detection of lung cancer using MTANN (Massive Training Artificial Neural Network) based soft tissue technique

Abstract Background An existing computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer. Method In the proposed computer aided detection (CAD) system, this issue has been resolved by creating MTANN based soft tissue technique from the lung segmented x-ray image. X-ray images are downloaded using JSRT(Japanese society of radiological technology) image set. JSRT image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represent subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists. Result An existing computer aided detection (CAD) scheme attained 66.42% (93/140) sensitivity having 2.5 false positives (FPs) per image. Utilizing MTANN based soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) at one false positive rate). Conclusion In particular, proposed computer aided detection (CAD) system using soft tissue technique determine sensitivity in support of subtle nodules (14/42=33.33%) is statistically higher than CAD (13/42=30.95%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition.