The Effect of Different Feature Selection Methods for Classification of Melanoma

Maiti, Ananjan; Chatterjee, Biswajoy

doi:10.1007/978-981-33-6966-5_13

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rather than focusing just on improving feature extraction, feature selection is also considered to increase performance. For instance, in [ 44 ], several feature selection methods are proposed such as gradient boosting, statistical methods, and optimization algorithms such as PSO. Likewise, in [ 45 ], the deep learning method by using U-Net along with stochastic weighted averaging for the segmentation task of melanoma is suggested.…”

Section: Related Workmentioning

confidence: 99%

An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks

Ali

Naz

Zaffar

et al. 2023

Sensors

View full text Add to dashboard Cite

Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%