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Lung cancer is a leading cause of death in most countries of the world. Sinceprompt diagnosis of tumors can allow oncologists to discern their nature, typeand the mode of treatment, tumor detection and segmentation from CT Scan imagesis a crucial field of study worldwide. This paper approaches lung tumorsegmentation by applying two-dimensional discrete wavelet transform (DWT) onthe LOTUS dataset for more meticulous texture analysis whilst integratinginformation from neighboring CT slices before feeding them to a DeeplySupervised MultiResUNet model. Variations in learning rates, decay andoptimization algorithms while training the network have led to different diceco-efficients, the detailed statistics of which have been included in thispaper. We also discuss the challenges in this dataset and how we opted toovercome them. In essence, this study aims to maximize the success rate ofpredicting tumor regions from two dimensional CT Scan slices by experimentingwith a number of adequate networks, resulting in a dice co-efficient of 0.8472.

Segmentation of Lung Tumor from CT Images using Deep Supervision