DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

Abstract

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel ‘F’ Flag feature for early detection. This novel ‘F’ indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN ‘F’ Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the ‘F’ indicator, resulting in the highest specificity of + 7.34%.

Diagnosis of Medical Images Using Convolutional Neural Networks

“…These filters analyze the input image for patterns and features at various levels of abstraction, capturing both local and global structures [5]. CNNs can learn more complicated features by stacking many convolutional layers and pooling layers, allowing them to successfully differentiate between distinct classes or categories of input [6]. CNNs have been used to analyze a variety of medical imaging modalities, including X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, ultrasound images, and histopathological slides [7] [8].…”

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

“…Using both polarized and nonpolarized dermoscopy to get medical images improves the efficacy of the recognition. The recognition of skin cancer can then be aided by processing these images using artificial intelligence (AI) techniques, as it is shown in [9][10][11][12][13].…”