Gastric Carinoma Detection using Hybrid Model based TL

Gastric Carinoma Detection using Hybrid Model based TL

Turlapati Kavya sri¹, Satuluri Likitha Naga Vigneswari¹, Kommuri Vijay Manikanta¹, Manikonda V Srikar Janardhan Rao¹, Talluri V Lakshmi Bhavani Lalith¹, Venkat Sainadh Reddy¹, Mohammed Uzair Dastagir²

¹School of Computer Science and Engineering, VIT-AP University, G-30, Inavolu, Beside AP Secretariat Amaravati, Andhra Pradesh 522237

^{, 2}Department of Computer Science and Engineering, Amritha Viswa Vidyapetham, Amritapuri, India.

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Abstract -Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research. However, due to the amplification of noise during the upsampling steps, state-ofthe-art methods often fail at reconstructing high-resolution images from noisy versions of their low-resolution counterparts. However, this is especially important for images from unknown cameras with unseen types of image degradation. In this work, we propose to jointly perform denoising and super-resolution. To this end, we investigate two architectural designs: "in-network" combines both tasks at feature level, while "pre-network" first performs denoising and then super-resolution. Our experiments show that both variants have specific advantages: The in-network design obtains the strongest results when the type of image corruption is aligned in the training and testing dataset, for any choice of denoiser. The prenetwork design exhibits superior performance on unseen types of image corruption, which is a pathological failure case of existing super-resolution models. We hope that these findings help to enable super-resolution also in less constrained scenarios where source camera or imaging conditions are not well controlled.

Key Words: Super-resolution, Denoising, Deep learning, Image enhancement