  Lung cancer is a leading cause of death in most countries of the world. Since
prompt diagnosis of tumors can allow oncologists to discern their nature, type
and the mode of treatment, tumor detection and segmentation from CT Scan images
is a crucial field of study worldwide. This paper approaches lung tumor
segmentation by applying two-dimensional discrete wavelet transform (DWT) on
the LOTUS dataset for more meticulous texture analysis whilst integrating
information from neighboring CT slices before feeding them to a Deeply
Supervised MultiResUNet model. Variations in learning rates, decay and
optimization algorithms while training the network have led to different dice
co-efficients, the detailed statistics of which have been included in this
paper. We also discuss the challenges in this dataset and how we opted to
overcome them. In essence, this study aims to maximize the success rate of
predicting tumor regions from two dimensional CT Scan slices by experimenting
with a number of adequate networks, resulting in a dice co-efficient of 0.8472.
