Feature Extraction Process Research Articles

A Data-Driven Feature Extraction Process of Interleaved DC/DC Converter Due to the Degradation of the Capacitor in the Aircraft Electrical System

In recent years, preventive maintenance has emerged as a focal point of research in the aerospace field. The concept of equipment maintenance, exemplified by prognosis and health management (PHM), has permeated every aspect of development and design. Extracting degradation features presents a fundamental and challenging task for health assessment and remaining useful life prediction. To facilitate the efficient operation of the incipient fault diagnosis model, this paper proposes a data-driven feature extraction process for converters, which consists of two main stages. First, feature extraction and comparison are conducted in the time domain, frequency domain, and time–frequency domain. By employing wavelet decomposition and the Hilbert transform method, a highly correlated time–frequency domain feature is obtained. Second, an improved feature selection approach that combines the ReliefF algorithm with the correlation coefficient is proposed to effectively minimize redundancy within the feature subset. Furthermore, an incipient fault diagnosis model is established using neural networks, which verifies the effectiveness of the data-driven feature extraction process presented herein. Experimental results indicate that this method not only maintains fault diagnosis accuracy but also significantly reduces training time.

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accurate prediction and prioritization of highly immunogenic and optimized MHC-I and MHC-II T-cell epitopes for cancer vaccine development and immunotherapy. Utilizing a curated dataset of experimentally validated epitopes and employing sophisticated machine learning techniques, VaxOptiML features three models: epitope prediction from target sequences, personalized HLA typing, and prioritizationthe predicted epitopesbased on immunogenicity scores. Our rigorous data extraction, cleaning, and feature extraction processes, coupled with model building, yield exceptional accuracy, sensitivity, specificity, and F1 score, surpassing existing prediction methods. Comprehensive visual representations underscore VaxOptiML's robustness and efficacy in accelerating epitope discovery and vaccine design for cancer immunotherapy. Deployed via Streamlit for public use, VaxOptiML enhances accessibility and usability for researchers and clinicians, demonstrating significant potential in cancer immunotherapy.

Resilient Semi-Supervised Meta-Learning Network based on wavelet transform and K-means optimization for fluid classification

In the field of geological exploration, accurately distinguishing between different types of fluids is crucial for the development of oil, gas, and mineral resources. Due to the scarcity of labeled samples, traditional supervised learning methods face significant limitations when processing well log data. To address this issue, this paper presents a novel fluid classification method known as the Resilient Semi-Supervised Meta-Learning Network (RSSMLN) based on wavelet transform and K-means optimization, which combines the advantages of few-shot learning and semi-supervised learning, aiming to optimize fluid recognition in well log data. Initially, this study employs a small set of labeled samples to train the initial model and utilizes pseudo-label generation and K-means clustering to optimize prototypes, thereby enhancing the model's accuracy and generalization ability. Subsequently, during the feature extraction process, wavelet transform preprocessing techniques are introduced to enhance the time-frequency feature representation of well log data through multi-scale decomposition. This process effectively captures high-frequency and low-frequency features, providing structured information for subsequent convolution operations. By employing a dual-channel heterogeneous convolutional kernel feature extractor, RSSMLN can effectively capture subtle features of the fluids and significantly improve classification accuracy. Experimental results indicate that compared to various standard deep learning models, RSSMLN achieves superior performance in fluid identification tasks. This research provides a reliable solution for few-shot fluid recognition in oilfield applications and offers scientific support for resource exploration and evaluation.

Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection

<span lang="EN-US">As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective attention graph followed by multi-scale feature synthesis is employed to retain precise and spoof indicative feature vectors at the classification layer. The proposed method addresses the significant challenge of distinguishing genuine speech from replayed recordings. The validation of the proposed model is done on the ASVSpoof2019 dataset to demonstrate the efficacy of the proposed approach. The proposed system outperforms existing methods, achieving a lower equal error rate (EER) of 0.015 and a reduced tandem detection cost function (t-DCF) of 0.503. The comparative outcome exhibits the robustness of the method in identifying replay attacks.</span>

Intelligent characterization and robustness quantification of frozen soil strength images using a multi-module fusion strategy

In frozen wall engineering, traditional detection methods are difficult to implement for real-time monitoring of strength parameters, due to their intermittent nature. This makes it difficult to provide timely warnings and effective responses to potential risks of frozen walls, highlighting the urgent need for a method that can continuously and accurately monitor the strength of frozen walls. An image data-driven intelligent identification method for frozen soil strength based on convolutional neural networks is proposed. By capturing images of cured samples from multiple angles and conducting uniaxial compressive strength tests, the collected strength data were divided into 12 categories, creating an image dataset for deep learning model training. A Resnet-34 deep learning model combined with global attention and downsampling showed excellent performance in comparison with a series of basic models, with an accuracy of 93.3 % and no overfitting. The accuracy of the deep learning model was assessed using adversarial attack and defense strategies, serving as an indicator of robustness. The improved model exhibited better robustness in comparative analyses. Using SHapley Additive exPlanations (SHAP) value analysis, the feature extraction process of the convolutional neural network in recognizing frozen soil strength was investigated and clarified, confirming the model's ability to identify and extract key features in frozen soil images, such as particle size, texture, cracks, and ice crystal distribution patterns. This technology offers a cutting-edge method for real-time tracking of frozen wall conditions and early disaster warnings, significantly enhancing the safety and success rate of construction projects under freezing conditions.

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

6D Pose Estimation of Industrial Parts Based on Point Cloud Geometric Information Prediction for Robotic Grasping

In industrial robotic arm gripping operations within disordered environments, the loss of physical information on the object’s surface is often caused by changes such as varying lighting conditions, weak surface textures, and sensor noise. This leads to inaccurate object detection and pose estimation information. A method for industrial object pose estimation using point cloud data is proposed to improve pose estimation accuracy. During the feature extraction process, both global and local information are captured by integrating the appearance features of RGB images with the geometric features of point clouds. Integrating semantic information with instance features effectively distinguishes instances of similar objects. The fusion of depth information and RGB color channels enriches spatial context and structure. A cross-entropy loss function is employed for multi-class target classification, and a discriminative loss function enables instance segmentation. A novel point cloud registration method is also introduced to address re-projection errors when mapping 3D keypoints to 2D planes. This method utilizes 3D geometric information, extracting edge features using point cloud curvature and normal vectors, and registers them with models to obtain accurate pose information. Experimental results demonstrate that the proposed method is effective and superior on the LineMod and YCB-Video datasets. Finally, objects are grasped by deploying a robotic arm on the grasping platform.

Enhancing Image Stitching Algorithms with SIFT Feature Detection

Image stitching technology plays a significant role in the fields of computer vision and image processing, with applications ranging from panoramic photography to virtual reality (VR), augmented reality (AR), medical diagnostics, and autonomous vehicle technology. As technology advances, the demand for high-quality, real-time panoramic images provided by image stitching technology continues to grow. This study aims to implement an image stitching method based on the Scale-Invariant Feature Transform (SIFT) feature point detection algorithm, combined with the Random Sampling Consensus (RANSAC) algorithm and the calculation of the homography matrix to automatically stitch two images. This paper elaborates on the entire process of image feature extraction, feature matching, homography matrix calculation, and image fusion, and compares different fusion modes. The experimental results show that the method can achieve seamless image stitching in some cases, its performance in complex scenes such as crowds or traffic flows is average. This study provides new perspectives and methods for the application of image stitching technology.

MSTD: a framework for rolling bearing fault diagnosis based on multi-scale and soft-threshold denoising

ABSTRACT In real industrial environments, the data collected is often subject to noise interference, which leads to challenges for traditional deep learning fault diagnosis models, including insufficient autonomous learning capabilities, complex feature extraction processes, and sensitivity to noise. To address these issues, this paper proposes a bearing fault diagnosis convolutional neural network based on multi-scale and soft-threshold denoising (MSTD). In this model, Poly Kernel Feature Fusion (PKFF) is utilised to extract high-quality features from raw vibration data, enhancing the model’s ability to recognise complex fault patterns; Residual anchor attention (RAA) reduces redundancy and irrelevant information between channels, optimising inter-channel information to ensure the model’s effectiveness when handling multi-channel data; Soft threshold CG (ST-CG) utilises a soft-threshold denoising method to suppress noise after feature extraction, thereby more clearly revealing fault characteristics. Soft-threshold processing not only enhances the model’s noise resistance but also preserves critical fault information, enabling the model to maintain high diagnostic accuracy even under low signal-to-noise ratio conditions. The proposed model demonstrates outstanding performance on the CWRU and MFS-MG datasets, exhibiting superior feature extraction capabilities and robustness in high-noise environments compared to other models, significantly improving diagnostic accuracy and noise resistance.

Design of an epidemic prevention and control bracelet system integrated with convolutional neural networks: Promote real-time physiological feedback and adaptive training in remote physical education

This study aims to design an epidemic prevention and control bracelet system that integrates convolutional neural network (CNN). This system can collect and process the user’s physiological index data in real time, especially in the remote physical education scene, and provide learners with immediate physiological index feedback and personalized adaptive training suggestions through accurate human action recognition (HAR) technology. One-dimensional acceleration signal is converted into two-dimensional image, and CNN’s powerful feature extraction and classification ability is used to effectively solve the problem that manual feature extraction is complex and nonlinear features are difficult to capture. By considering the joint action trajectory in the time window, a dynamic Recurrence Plot (RP) is constructed to capture the dynamic changes among joints. To input recursive graph data into CNN, it needs to be converted into image form. In the task of HAR, CNN can automatically learn useful features from images without manually designing features. It can not only effectively extract features from images, but also be directly used in classification tasks. Experimental results show that compared with other algorithms, the proposed RP + CNN model has the best performance in action recognition, with an accuracy of 96.89% and a F1 value of 86.76%. RP captures the dynamic patterns and periodic behaviors in time series by visualizing the repeated appearance of system states over time. The RP + CNN model is used to extract and classify human action features, which significantly improves the accuracy and efficiency of HAR. This innovative method not only simplifies the complex process of traditional manual feature extraction, but also enhances the system’s ability to identify nonlinear and complex action patterns, which provides strong technical support for remote physical education.

A novel U-LSTM-AFT model for hourly solar irradiance forecasting

Countries emphasize the development of renewable energy to combat climate change, ensure energy security, and promote sustainable economic growth. Grid-connected photovoltaic (PV) power generation, a key to energy transition, can significantly reduce power generation costs. Still, its output power is unstable due to the weather, so an accurate solar irradiance forecast is crucial. The current hybrid model mainly suffers from inadequate feature extraction and insufficient time-dependent processing when dealing with complex time series data, which affects prediction accuracy. Therefore, a new solar irradiance forecast model, U-Shaped LSTM-AFT (U-LSTM-AFT) is proposed in this paper, which draws on the architecture and ideas of U-Net. The model improves the efficiency of feature extraction through the up-sampling and down-sampling modules while combining with smaller pooling kernels to optimize feature processing further. In addition, the model introduces the Long Short-Term Memory Network (LSTM) and Attention-Free Transformer (AFT) to enhance prediction accuracy and achieve more efficient forecast performance. The method was validated using three real-world irradiance datasets from diverse US locations representing different climate types. The experimental results demonstrate that the proposed U-LSTM-AFT model achieves higher forecasting accuracy in predicting PV power generation than traditional models like LSTM and U-Net, with significant performance improvements. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), R-squared (R2), and Forecast Skill (FSnRMSE) are the performance evaluation metrics of this forecasting scheme. The three datasets reached mean values of 23.076 W/m2, 50.142 W/m2, 0.046, 98.680 %, and 0.238, respectively.

Bearing fault diagnosis method for unbalance data based on Gramian angular field

In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods.

A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis

ABSTRACT Rolling bearing fault diagnosis enhances equipment reliability, reduces maintenance costs, and enables effective non-destructive testing (NDT). However, current research often emphasizes model design and performance optimization, overlooking the long-term dependencies of fault signals and the need for interpretability. This study proposes a rolling bearing fault diagnosis model utilizing a time-series fusion transformer with interpretability analysis. The model introduces multi-scale feature adaptive fusion to automatically capture and integrate features across different scales, enhancing the global pattern detection in time-series data. A dynamic patch auto-encoder module transforms feature embeddings into a low-dimensional space to better retain local information. The model’s design, particularly the decoding layer of the time-series Transformer, is optimized with a multi-head self-attentive mechanism, and multi-dimensional attention weights visualization methods are employed to clarify the fault feature extraction process. Quantitative visualizations throughout training improve interpretability and insight into learning dynamics. Experimental results indicate that this model surpasses state-of-the-art approaches on benchmark datasets, proving its generalizability and robustness in diverse testing scenarios.

HNet: A deep learning based hybrid network for speaker dependent visual speech recognition

Visual Speech Recognition (VSR) is a popular area in computer vision research, attracting interest for its ability to precisely analyze lip motion and seamlessly convert them into textual representation. VSR systems leverage visual features to augment the understanding of automated speech and predict text. VSR finds various applications, including enhancing speech recognition in scenarios with degraded acoustic signals, aiding individuals with hearing impairments, bolstering security by reducing reliance on text based passwords, facilitating biometric authentication for liveness detection, and enabling underwater communications. Despite the various techniques proposed for improving the resilience and precision of automatic speech recognition, VSR still has challenges like homophones of words, gradient descent issues with varying sequence lengths, and lip reading demands accounting for short and long range correlations between consecutive video frames. We have proposed a hybrid network (HNet) with multilayered three dimensional dilated convolution neural network (3D-CNN). The spatio-temporal feature extraction process will be facilitated by dilated 3D-CNN. HNet integrates two bidirectional recurrent neural networks (BiGRU and BiLSTM) to process the feature sequences bidirectionally to establish the temporal relationship. The fusion of BiGRU-BiLSTM capabilities allows the model to process feature sequences more comprehensively and effectively. The proposed work focuses on face based biometric authentication for liveness detection using the VSR model to boost security against face spoofing. The existing face based biometric systems are widely used for individual authentication and verification but are still vulnerable to 3D masks and adversarial attacks. The VSR system can be added to existing face based verification systems as a second level authentication technique to identify a person with liveness. The working ideology of the VSR system will be based on the challenge response technique, where a person has to pronounce the passcode silently displayed on the screen. The VSR model assesses its effectiveness using word error rate (WER), which matches the pronounced passcode to the one presented on the screen. Overall, the proposed work aims to enhance the accuracy of VSR so that it can be combined with existing face based authentication systems. The proposed system outperforms the existing VSR system and obtained 1.3% WER. The significance of the proposed hybrid model is that it efficiently captures temporal dependencies, enhancing context embedding, improving robustness to input variability, reducing information loss, and enhancing performance and accuracy in modeling and analyzing passcode pronunciation patterns.

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems.

Q-Learning-Incorporated Robust Relevance Vector Machine for Remaining Useful Life Prediction

Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM faces the issue of poor robustness, which is mainly manifested as poor prediction accuracy and difficulty in fitting when the predicted data fluctuate greatly. This is due to weights and random errors following Gaussian distributions, which are highly sensitive to outliers. Also, the traditional model training process heavily relies on an additional feature extraction process, which suffers from the problem of effective data loss as well as the risk of overfitting. Thus, a robust regression framework against outliers is developed by incorporating t-distribution into the RVM. And a Q-learning (QL) algorithm is embedded into the constructed robust RVM model to replace the feature extraction process. In addition, this paper firstly predicts the degradation trend of RUL to enhance the accuracy and interpretability of RUL prediction. Finally, a comparative experiment on the performance degradation of capacitors in the traction system is designed, and the root mean square errors for the QL-RRVM, QL-RVM, RRVM, and RVM models are obtained as 0.751, 8.599, 38.316, and 41.892, respectively. The experimental results confirm the superiority of the proposed method.

A flame image soft sensor for oxygen content prediction based on denoising diffusion probabilistic model

High-precision oxygen content measurement is crucial for statistical analysis of combustion chemical reaction. Deep learning based soft sensor is a new class of intelligent tools for monitoring combustion oxygen content. But in the actual production, data for sensors are often insufficient. A new soft sensing model is proposed to display the excellent performance of denoising diffusion probabilistic model (DDPM) in data generation. Firstly, a UNet based soft sensor is designed by integrating self-attention mechanism into the convolution layers. Then, a denoising loss function is designed to link the feature extraction process of soft sensor model with the reverse denoising process of DDPM, and the noise prediction neural network of DDPM is used to improve the feature extractability of the soft sensor model. Finally, the proposed model is compared with common models. The effectiveness and superiority of the proposed soft sensing model for oxygen content prediction, especially in the case with a small sample size, are both confirmed by the results.

Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism

Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentation model of northern corn leaf blight, this study proposes a segmentation method based on an improved U-Net network model. By introducing a convolutional layer and maximum pooling layer to a VGG19 network, the channel attention module and spatial attention module (CBAM) are fused, and the squeeze excitation (SE) attention mechanism is combined. This enhances image feature decoding, integrates feature maps of each layer, strengthens the feature extraction process, expands the sensory fields and aggregates context information, and reduces the loss of location and dense semantic information caused by the pooling operation. Findings from the study show that the proposed NCLB-Net has significantly improved the MIoU and PA indexes, reaching 92.43% and 94.71%, respectively. Compared with the traditional methods, U-Net, SETR, DAnet, OCnet, PSPNet, etc., the MIoU is improved by 20.81%, 16.10%, 9.79%, 5.27%, and 11.06%, and the PA is improved by 11.49%, 8.18%, 9.54%, 13.11%, and 6.26%, respectively.

Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.

Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field. Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances. In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features. The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness. The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.

DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection

Skin cancer is a common type of cancer worldwide. Early diagnosis of skin cancer can reduce the risk of death by increasing treatment success. However, it is challenging for dermatologists or specialists because the symptoms are vague in the early stages and cannot be noticed by the naked eye. This study examines digital diagnostic techniques supported by artificial intelligence, focusing on early skin cancer detection and two methods have been proposed. In the first method, DSCIMABNet deep learning architecture was developed by combining multi-head attention and depthwise separable convolution techniques. This model provides flexibility in learning the dataset's local features, abstract concepts, and long-term relationships. The DSCIMABNet model and modern deep learning models trained on ImageNet are proposed to be combined with the ensemble learning method in the second method. This approach provides a comprehensive feature extraction process that will increase the performance of the classification process with ensemble learning. The proposed approaches are trained and evaluated on the ISIC 2018 dataset with image enhancement applied in preprocessing. In the experimental results, DSCIMABNet achieved 84.28% accuracy, while the proposed hybrid method achieved 99.40% accuracy. Moreover, on the Mendeley dataset (CNN for Melanoma Detection Data), DSCIMABNet achieved 92.58% accuracy, while the hybrid method achieved 99.37% accuracy. This study may significantly contribute to developing new and effective methods for the early diagnosis and treatment of skin cancer.