Integrating quantum algorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This integration can enhance speed and efficiency when performing complex computations. However, hybrid quantum models, particularly on Quantum Convolutional Neural Networks (QCNNs) face two significant drawbacks: the placement of the quantum convolutional layer before the model architecture and the lack of integration of the quantum layer within the training process. These disadvantages reduce the robustness and reproducibility of the models. This study proposes that integrates the quantum layer into the quantum layer to address these shortcomings. We present a comparative analysis between a hybrid quantum deep learning model, which includes a trainable quantum layer, and its classical counterpart for the classification of skin cancer dermatoscopic images. The hybrid model attains 0.7865 of accuracy, a recall of 0.7321, a precision of 0.7268, and an F1 Score of 0.7288, while the classical model reaches an accuracy, recall, precision, and F1 Score of 0.8510, 0.8472, 0.8495, and 0.8447. The hybrid model achieves comparable results to its classical counterpart and demonstrates the advantages of weight adjustment in quantum layers and their potential in improving medical imaging analysis.
Skin cancer is one of the most common and lethal diseases in America, and its early detection and treatment is the best approach to protect the lives of those afflicted. Computer-Aided Diagnosis systems have been implemented for decades as evidence of intelligent methods applied in the medical field. In recent years, machine and deep learning fields have shown great potential in medical diagnosis, particularly in skin cancer classification. These methods enable the automated extraction of complex input features, such as those in medical imagery. Therefore, with the advent of quantum computing, it is now possible to perform complex computations with increased speed and efficiency. This study explores the application of quantum machine learning in the classification of skin cancer using the ResNet50 model, a deep convolutional neural network for RGB images. The research employs a quantum-enhanced version of ResNet50 using a quanvolutional layer that the input skin lesion images go through and compare its performance with the classical ResNet50 version. We show comparative experiments, and the results indicate that further experiments are needed using more extensice datasets and different quantum deep learning architectures.
Traffic sign detection is a crucial task in autonomous driving systems. Due to its importance, several techniques have been used to solve this problem. In this work, the three more common approaches are evaluated. The first approach uses a model of the traffic sign which is based in color and shape. The second one enhances the image model of the first approach using K-means for color clustering. The last approach uses convolutional neural networks designed for image detection. The LISA Traffic Sign Dataset was used which it was divided into three superclasses: prohibition, mandatory, and warning signs. The evaluation was done using objective metrics used in the state-of-the-art.
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