2 results match your criteria: "Department of Electronics and Instrumentation Engineering Easwari Engineering College[Affiliation]"
Background: Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models.
View Article and Find Full Text PDFCombining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification.
Concurr Comput
July 2022
Department of Computer and communication Sri Sairam Institute of Technology Chennai India.
Article Synopsis
- A COVID-19 detection and classification framework is developed using a combination of an optimized AlexNet convolutional neural network and a random forest classifier, utilizing a dataset from the Joseph Paul Cohen database.
- Image preprocessing techniques, specifically fuzzy gray level difference histogram equalization (FGLHE) and fuzzy stacking, are employed to enhance image quality and reduce noise before training the model.
- The proposed method (ADCNN-ASA-RFC) shows significant improvements in accuracy, specificity, and sensitivity compared to existing algorithms, demonstrating its effectiveness in accurately diagnosing COVID-19.