Optimization Convolutional Neural Network for Automatic Skin Lesion Diagnosis Using a Genetic Algorithm

Abstract

Examining and predicting skin cancer from skin lesion images is challenging due to the complexity of the images. Early detection and treatment of skin lesion disease can prevent mortality as it can be curable. Computer-aided diagnosis (CAD) provides a second opinion for dermatologists as they can classify the type of skin lesion with high accuracy due to their ability to show various clinical identification features locally and globally. Convolutional neural networks (CNNs) have significantly improved the performance of CAD systems for medical image segmentation and classifications. However, tuning CNNs are challenging since the search space of all possible hyperparameter configurations is substantially vast. In this paper, we adopt a genetic algorithm to automatically configure a CNN model for an accurate, reliable, and robust automated skin lesion classification for early skin lesion diagnosis. The optimized CNN model uses four public datasets to train and be able to detect abnormalities based on skin lesion features in different orientations. The model achieves the best scores for each of the DICE coefficients, precision measure, and F-score. These scores compare better than other existing methods. Considering the success of this optimized model, it could be a valuable method to implement in clinical settings.

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

“…Following the success of CNN in image processing in other real-world applications, it is also being explored as a key and robust method for applications in clinical settings [2,3]. In this review, we compiled recently improved components of deep CNN architectures, popular frameworks, activation functions, preprocessing approaches, publicly available datasets, ensemble methods and optimization techniques that are being applied for medical image understanding.…”

“…Metaheuristic optimization techniques [103] or SHO metaheuristic optimization for fine-tuning the weights, biases and hyperparameters [104] • The orthogonal array tuning method [2], the adaptive hyperparameter tuning and the covariance matrix adaptation evolution strategy.…”

“…• Receiver operating characteristic (ROC) is a graphical representation of the trade-off between sensitivity and specificity for different classification thresholds. It is used to evaluate the performance of CNNs in binary classification tasks [2,5]. • Area under the curve (AUC) is a summary measure of the ROC curve representing the probability that a randomly chosen negative instance will be ranked lower than a randomly chosen positive instance.…”

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

“…Furthermore, the emergence of deep learning methods, i.e., CNNs, has shown the capacity for transformation in analyzing healthcare images. The capability of CNNs to identify complicated characteristics from visuals and to make decisions based on information presents an exciting chance for improvement in the discipline of skin diseases 9 . The convergence of urgent medical necessity and state-of-the-art technology constitutes the primary impetus for our investigation, compelling us to construct a sophisticated model for identifying skin cancer that harnesses the capabilities of deep learning and optimization methodologies.…”

An Effective Approach to Detect Melanoma Using Semantic Mathematical Model and Modified Golden Jackal Optimization Algorithm

“…However, accurate screening and classification of image lesions remain a challenge for dermatologists due to improper image boundaries and varying sizes [7,8]. The growth of machine learning and deep learning techniques acts as an effective solution to detect skin cancer from medical images.…”