Abstract
Computer Aided Diagnosis (CAD) is an ever-growing field as it facilitates the effective diagnosis of diseases at an early stage. This type of diagnosis system includes segmentation of abnormal tissues, polyps, disease staging and classification etc. All the aforementioned CAD system tasks rely directly upon the quality of the medical images that are taken as input. Image quality depends on various parameters, out of which contrast is an important attribute. The task of segmenting gallbladder and soft tissues from Magnetic Resonance Cholangiopancreatography (MRCP) images has significance in the investigation of many pancreatico-biliary disorders. Efficacy of the segmentation of the structures from MRCP images requires the parameter of contrast to be maintained to a confined level. Though the performance of artificial intelligence in medical image analysis seems compromising, the concepts of reliability and explainability for the developed algorithms are needed as it deals with health aided systems. Thus, through this research we developed an explainable approach that articulates the various soft tissues present in the MRCP images by clustering them into groups. The use of Tree Seed Algorithm (TSA), a nature inspired optimization algorithm with a deep neural network, provides a perfect learning machine that creates exact clusters even in low contrast MRCP images. Unlike the conventional black box method, this machine provides nearest neighbors and clusters that are responsible for the lowest distance between them. The model’s interpretability can be explained by Local Interpretable Model-agnostic Explanations (LIME). The efficacy of the trained network is imposed on a 120 MRCP image dataset obtained from a diagnostic center in Tamilnadu. The proposed network provides improved accuracy when compared with other Deep learning models such as ResNet50, DenseNet201.
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Muneeswaran, V., Nagaraj, P., Ijaz, M.F. (2022). An Articulated Learning Method Based on Optimization Approach for Gallbladder Segmentation from MRCP Images and an Effective IoT Based Recommendation Framework. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_8
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