Hand-crafted Features Research Articles

A novel framework for concealed weapons detection using passive terahertz imaging

ABSTRACT In this work, a novel framework for non-destructive, non-intrusive and completely automatic concealed weapon detection under human clothing for entry control and security check applications using passive terahertz (THz) images has been given. The technique models the problem of concealed weapon detection as that of binary segmentation, where the pixels corresponding to concealed weapons are foreground while the remaining are background. The core idea of the proposed framework is to first oversegment the THz image into superpixels and subsequently multiple handcrafted features are extracted from each superpixel. Finally, a machine learning-based classifier is used to perform binary classification thereby classifying each superpixel as foreground or background such that each pixel in a foreground superpixel is a foreground pixel and vice-versa for background. It is worth mentioning that this technique does not require a perfect segmentation to extract features from, rather an oversegmentation suffices. Furthermore, both global features like those based on image saliency and local features like those based on intensity are extracted considering the image properties. The detailed experiments and comparative analysis confirm that the proposed technique efficiently detects concealed weapons, exhibiting superior performance compared to state-of-the-art binary segmentation methods for the purpose.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

Hybrid Convolution-Based Efficientnet-Based Hand Gesture Recognition Framework with Optimized Algorithm

The difficulties in communication and hearing are an important concern for deaf–dumb people, which stop access to their essential and basic needs. Many findings have been made to address sign languages even though this challenging problem is not still solved. Many methods aimed to propose vision-based classifiers through identical pattern investigation tasks by obtaining the difficult handcraft feature descriptions of gestures from the gathered images. However, the efficacy of all those models is less for performing with a huge signbook captured from uncontrolled and complex background conditions. So, an effective Indian Sign Language (ISL) classification method is developed by an advanced deep learning approach. At first, the hand gesture images are obtained from the data source. Only the image of the hand, even from a complicated background, is extracted from the obtained image. The features are extracted using the Scale-Invariant Feature Transform (SIFT) method and Multiscale Vision Transformer (MVT). Then, the extracted features are fed to the Hybrid Convolution-based EfficientNet (HCEN) model. The hyper-parameters in the developed HCEN model are tuned using the implemented Adaptive Political Optimizer (APO) algorithm. The recognized hand signs are obtained from the suggested HCEN model. Various experiments are conducted to determine the performance of the suggested deep learning-based hand gesture recognition model.

Hand-Crafted Features With a Simple Deep Learning Architecture for Sensor-Based Human Activity Recognition

Hand-Crafted Features With a Simple Deep Learning Architecture for Sensor-Based Human Activity Recognition

Deep Feature Extraction with SVM Classifier for Brain Tumor Classification

Recently, methods for classifying brain tumors have used Deep Learning (DL), in particular, Convolutional Neural Networks (CNNs), to obtain promising results. Unlike conventional Machine Learning (ML) algorithms, CNNs can automatically extract features from images; they do not, however, need the extraction regarding hand-crafted features. Many obstacles reduce the accuracy of brain tumor classification systems; to address such obstacles, we propose a DL-based feature extraction method for brain tumor classification. A total of two pretrained CNNs, VggNet-16 and ResNet-18, are applied as deep feature extractors in the scheme to accomplish deep feature extraction. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique has been first used for enhancing the images. Second, each separately pretrained CNN, VggNet-16 and ResNet-18, is used for extracting the deep features from Magnetic Resonance Imaging (MRI). Lastly, a Multiclass Support Vector Machines (Multiclass -SVM) classifier is used to complete a classification task. Tests conducted on the aforementioned datasets: the effectiveness of the suggested approach is demonstrated by the CE-MRI Figshare, the REMBRANDT, and the Brain Tumor Classification, where the pre-trained CNN models and SVM classifier enhance performance. Specifically, the pretrained CNN with SVM approach produces accuracy ranging from 95.28% to 98.62% across all data-sets.

Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges

Abstract Real-time Hand Gesture Recognition (HGR) has emerged as a vital technology in human-computer interaction, offering intuitive and natural ways for users to interact with computer-vision systems. This comprehensive review explores the advancements, challenges, and future directions in real-time HGR. Various HGR-related technologies have also been investigated, including sensors and vision technologies, which are utilized as a preliminary step in acquiring data in HGR systems. This paper discusses different recognition approaches, from traditional handcrafted feature methods to state-of-the-art deep learning techniques. Learning paradigms have been analyzed such as supervised, unsupervised, transfer, and adaptive learning in the context of HGR. A wide range of applications has been covered, from sign language recognition to healthcare and security systems. Despite significant developments in the computer vision domain, challenges remain in areas such as environmental robustness, gesture complexity, computational efficiency, and user adaptability. Lastly, this paper concludes by highlighting potential solutions and future research directions trying to develop more robust, efficient, and user-friendly real-time HGR systems.

Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score

BackgroundRecent studies have used basic epicardial adipose tissue (EAT) assessments (eg, volume and mean Hounsfield unit [HU]) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACEs). ObjectivesThe purpose of this study was to create novel, hand-crafted EAT features, “fat-omics,” to capture the pathophysiology of EAT and improve MACE prediction. MethodsWe studied a cohort of 400 patients with low-dose cardiac computed tomography calcium score examinations. We purposefully used a MACE-enriched cohort (56% event rate) for feature engineering purposes. We divided the cohort into training/testing sets (80%/20%). We segmented EAT using a previously validated, deep-learning method with optional manual correction. We extracted 148 initial EAT features (eg, morphologic, spatial, and HU), dubbed fat-omics, and used Cox elastic-net for feature reduction and prediction of MACE. Bootstrap validation gave CIs. ResultsTraditional EAT features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-indices 0.53/0.55/0.57, respectively). Significant improvement was obtained with the 15-feature fat-omics model (C-index = 0.69, test set). High-risk features included the volume-of-voxels-having-elevated-HU-[-50,-30-HU] and HU-negative-skewness, both of which assess high HU values in EAT, a property implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, with the high-risk group having an HR 2.4 times that of the low-risk group (P < 0.001). ConclusionsPreliminary findings indicate an opportunity to use finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

NeuroPpred-SHE: An interpretable neuropeptides prediction model based on selected features from hand-crafted features and embeddings of T5 model

Neuropeptides are the most ubiquitous neurotransmitters in the immune system, regulating various biological processes. Neuropeptides play a significant role for the discovery of new drugs and targets for nervous system disorders. Traditional experimental methods for identifying neuropeptides are time-consuming and costly. Although several computational methods have been developed to predict the neuropeptides, the accuracy is still not satisfactory due to the representability of the extracted features. In this work, we propose an efficient and interpretable model, NeuroPpred-SHE, for predicting neuropeptides by selecting the optimal feature subset from both hand-crafted features and embeddings of a protein language model. Specially, we first employed a pre-trained T5 protein language model to extract embedding features and twelve other encoding methods to extract hand-crafted features from peptide sequences, respectively. Secondly, we fused both embedding features and hand-crafted features to enhance the feature representability. Thirdly, we utilized random forest (RF), Max-Relevance and Min-Redundancy (mRMR) and eXtreme Gradient Boosting (XGBoost) methods to select the optimal feature subset from the fused features. Finally, we employed five machine learning methods (GBDT, XGBoost, SVM, MLP, and LightGBM) to build the models. Our results show that the model based on GBDT achieves the best performance. Furthermore, our final model was compared with other state-of-the-art methods on an independent test set, the results indicate that our model achieves an AUROC of 97.8 % which is higher than all the other state-of-the-art predictors. Our model is available at: https://github.com/wenjean/NeuroPpred-SHE.

Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey.

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models, the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (− 0.017) and non-uniformity normalized (− 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.

Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods.

DCEnt‐PredictiveNet: A novel explainable hybrid model for time series forecasting

This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and forecasting. From the discrete wavelet transform coefficients of input time series data, computed four different handcrafted entropy features, which were then concatenated with deep features extracted using a modified DCNN. The concatenated deep and handcrafted feature vector was then fed to a SVR for prediction. The DCEnt-PredictiveNet framework was trained and tested on three time series datasets of real-world COVID-19, stock price and traffic information, and achieved mean absolute percentage errors of 0.03 %, 1.53 % and 11.41 % for daily cumulative COVID-19 positive cases, closing stock price, and hourly traffic (vehicle numbers) at one junction predictions, respectively. In addition, we incorporated local interpretable model-agnostic explanations and Shapley additive explanations methods into DCEnt-PredictiveNet to enable visualization of significant features that contributed to the model’s decision-making, thereby enhancing its explainability. Our DCEnt-PredictiveNet model yielded promising and interpretable forecasting results, which can facilitate advance resource planning in hospitals for incoming COVID-19 patients, stock market investment planning, and efficient traffic control management.

Limited Sample Radar HRRP Recognition Using FWA-GAN

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets.

Gastric Cancer Image Classification: A Comparative Analysis and Feature Fusion Strategies.

Gastric cancer is the fifth most common and fourth deadliest cancer worldwide, with a bleak 5-year survival rate of about 20%. Despite significant research into its pathobiology, prognostic predictability remains insufficient due to pathologists' heavy workloads and the potential for diagnostic errors. Consequently, there is a pressing need for automated and precise histopathological diagnostic tools. This study leverages Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. By utilizing both handcrafted and deep features and shallow learning classifiers on the GasHisSDB dataset, we conduct a comparative analysis to identify the most effective combinations of features and classifiers for differentiating normal from abnormal histopathological images without employing fine-tuning strategies. Our methodology achieves an accuracy of 95% with the SVM classifier, underscoring the effectiveness of feature fusion strategies. Additionally, cross-magnification experiments produced promising results with accuracies close to 80% and 90% when testing the models on unseen testing images with different resolutions.

Temporal-spectral-semantic-aware convolutional transformer network for multi-class tidal wetland change detection in Greater Bay Area

Coastal tidal wetlands are crucial for environmental and economic health, but facing threats from various environmental changes. Detecting changes of tidal wetlands is essential for promoting sustainable development in coastal areas. Despite extensive researches on tidal wetland changes, persistent challenges still exist. Firstly, the high similarity among tidal wetland types hinders the effectiveness of existing common indices. Secondly, many current methods, relying on hand-crafted features, are time-consuming and subject to personal biases. Thirdly, few studies effectively integrate multi-temporal and semantic information, leading to misinterpretations from environmental noise and tidal variations. In view of the abovementioned issues, we proposed a novel temporal-spectral-semantic-aware convolutional transformer network (TSSA-CTNet) for multi-class tidal wetland change detection. Firstly, to address spectral similarity among different tidal wetlands, we proposed a sparse second order feature construction (SSFC) module to construct more separable spectral representations. Secondly, to get more separable features automatically, we constructed temporal-spatial feature extractor (TSFE) and siamese semantic sharing (SiamSS) blocks to extract temporal-spatial-semantic features. Thirdly, to fully utilize semantic information, we proposed a center comparative label smoothing (CCLS) module to generate semantic-aware labels. Experiments in the Greater Bay Area, using Landsat data from 2000 to 2019, demonstrated that TSSA-CTNet achieved 89.20% overall accuracy, outperforming other methods by 3.75%–16.39%. The study revealed significant area losses in tidal flats, mangroves, and tidal marshes, decreased by 3148 hectares, 35 hectares, and 240 hectares, respectively. Among the cities in GBA, Zhuhai shows the most significant area loss with a total of 1626 hectares. TSSA-CTNet proves effective for multi-class tidal wetland change detection, offering valuable insights for tidal wetland protection.

Comparison of AI with and without hand-crafted features to classify Alzheimer's disease in different languages

BackgroundDetecting and analyzing Alzheimer's disease (AD) in its early stages is a crucial and significant challenge. Speech data from AD patients can aid in diagnosing AD since the speech features have common patterns independent of race and spoken language. However, previous models for diagnosing AD from speech data have often focused on the characteristics of a single language, with no guarantee of scalability to other languages. In this study, we used the same method to extract acoustic features from two language datasets to diagnose AD. MethodsUsing the Korean and English speech datasets, we used ten models capable of real-time AD and healthy control classification, regardless of language type. Four machine learning models were based on hand-crafted features, while the remaining six deep learning models utilized non-explainable features. ResultsThe highest accuracy achieved by the machine learning models was 0.73 and 0.69 for the Korean and English speech datasets, respectively. The deep learning models' maximum achievable accuracy reached 0.75 and 0.78, with their minimum classification time of 0.01s and 0.02s. These findings reveal the models’ robustness regardless of Korean and English and real-time diagnosis of AD through a 30-s voice sample. ConclusionNon-explainable deep learning models that directly acquire voice representations surpassed machine learning models utilizing hand-crafted features in AD diagnosis. In addition, these AI models could confirm the possibility of extending to a language-agnostic AD diagnosis.

PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.

The emergence of drug-resistant pathogens represents a formidable challenge to global health. Using computational methods to identify the antibacterial peptides (ABPs), an alternative antimicrobial agent, has demonstrated advantages in further drug design studies. Most of the current approaches, however, rely on handcrafted features and underutilize structural information, which may affect prediction performance. To present an ultra-accurate model for ABP identification, we propose a novel deep learning approach, PGAT-ABPp. PGAT-ABPp leverages structures predicted by AlphaFold2 and a pretrained protein language model, ProtT5-XL-U50 (ProtT5), to construct graphs. Then the graph attention network (GAT) is adopted to learn global discriminative features from the graphs. PGAT-ABPp outperforms the other fourteen state-of-the-art models in terms of accuracy, F1-score and Matthews Correlation Coefficient on the independent test dataset. The results show that ProtT5 has significant advantages in the identification of ABPs and the introduction of spatial information further improves the prediction performance of the model. The interpretability analysis of key residues in known active ABPs further underscores the superiority of PGAT-ABPp. The datasets and source codes for the PGAT-ABPp model are available at https://github.com/moonseter/PGAT-ABPp/.

Exploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images

Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.