Influence of GFP GAN on Melanoma Classification

Kumar, Ayushi; Vatsa, A. K.

doi:10.1109/isec54952.2022.10025075

Cited by 8 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our proposed work is the continuation of our effort to optimize the deep learning models. Imbibing Advanced preprocessing technique like Real ESRGAN 51–59 and GFP Gan 2,60–68 with base deep learning model like NASNet 69–78 and DenseNet 201 79–88 produces an significant results in context of validation accuracy. Results achieved via this pipe lining is tabulated in Table 1 for Real ESRGAN and GFP Gan respectively.…”

Section: Proposed Workmentioning

confidence: 99%

A novel deep learning technique for medical image analysis using improved optimizer

Agarwal,

Lohani,

Bist

2024

Health Informatics J

View full text Add to dashboard Cite

Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Objective: Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Method: Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Result and conclusion: Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.

show abstract