P. S. Arthy1,*, A. Kavitha2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511
Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge. The proposed deep learning architecture… More >
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