Papers using “Image Processing”

Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study

Joy Agboi, Paul Onoma, Arnold Adim Ojugo, Eferhire Valentine Ugbotu, Tabitha Chukwudi Aghaunor, Chris Chukwufunaya Odiakaose, Andrew Okonji Eboka, Amaka Patience Binitie, Peace Oguguo Ezzeh, Patrick Ogholuwarami Ejeh, Victor Ochuko Geteloma, Rebecca Okeoghene Idama, Anderson Ise Orobor, Chris Chukwudi Onochie, Chris Obaro Obruche

Lung cancer has since become the second largest direct cause of death when compared to other forms of cancer. Lung cancer is easily diagnosed via medical x-rays, MRIs and CT-scans. The use of learning schemes in identification of disease has become a veritable tool in medical frontiers. But, a major challenge with image classification – is attributed to the disease severity, and model’s ability to address feature enhancements in model designs for image processing. The models must capture intrinsic features with image enhancements in regions of interest – amongst other feats. Our study uses a median filter (AMF) with contrast enhancement (CLAHE) to normalize images, use a variation-mode decomposition as preprocessor to sustain multimodal image learning upheld by the Xception model. VMD handles complex image data to resolve mode mixing in its AMF and CLAHE. This is further upheld via localized context and hierarchical image representation in our CNN-based Xception model to yield improved training efficiency, model stability and generalization even with complex spatiotemporal dependencies inherent the image dataset. Our result shows the proposed method performs well in lung cancer detection, with significantly enhanced time convergence and reliability to yield a 1.000 Accuracy, 0.994 Precision, 0.989 Recall and 0.989 F1-score. Thus, the proposed model provides an effective image recognition and detection solution for lung cancer.