Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture
G. Prince Devaraj,
R. Ravi
Abstract:In this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature enhancement branch, and a global feature aggregation branch. We employ transposed convolution layers for upsampl… Show more
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