Method For Detection Of Breast Cancer Research Articles

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

Breast cancer is a major public health concern, and early detection and classification are essential for improving patient outcomes. However, breast tumors can be difficult to distinguish from benign tumors, leading to high false positive rates in screening. The reason is that both benign and malignant tumors have no consistent shape, are found at the same position, have variable sizes, and have high correlations. The ambiguity of the correlation challenges the computer-aided system, and the inconsistency of morphology challenges an expert in identifying and classifying what is positive and what is negative. Due to this, most of the time, breast cancer screen is prone to false positive rates. This research paper presents the introduction of a feature enhancement method into the Google inception network for breast cancer detection and classification. The proposed model preserves both local and global information, which is important for addressing the variability of breast tumor morphology and their complex correlations. A locally preserving projection transformation function is introduced to retain local information that might be lost in the intermediate output of the inception model. Additionally, transfer learning is used to improve the performance of the proposed model on limited datasets. The proposed model is evaluated on a dataset of ultrasound images and achieves an accuracy of 99.81%, recall of 96.48%, and sensitivity of 93.0%. These results demonstrate the effectiveness of the proposed method for breast cancer detection and classification.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

Study on breast cancer image detection and classification based on residual connected convolutional neural network (CNN)

This paper presents a method for breast cancer image detection and classification based on convolutional neural networks. While traditional breast cancer detection methods carry the risk of subjectivity and misclassification, machine learning can improve the accuracy and efficiency of detection by training algorithms to automatically identify breast cancer lesions in patients. The dataset of this paper includes 7909 microscopic images of breast tumor tissues with different magnifications collected from 82 patients, which are divided into two categories: benign and malignant tumors. In this paper, residual connection is designed for convolutional neural networks, which is a cross-layer connection method that can effectively solve the gradient vanishing and gradient explosion problems in deep neural networks, and at the same time, improve the generalization ability and training speed of the model. In this paper, the dataset is divided into training set, validation set and test set according to the ratio of 6:2:2. Through training and validation, the prediction accuracy of the obtained model on the test set reaches 85.5%, which achieves good prediction results in breast cancer image detection and classification. Eventually, the model's loss converged at 0.53 and the AUC was 0.832. The research results in this paper are of great significance for the early diagnosis and treatment of breast cancer. Automatic identification and classification of breast cancer images by machine learning algorithms can reduce the subjective judgment of doctors, improve the accuracy and efficiency of detection, and provide important support for the early diagnosis and treatment of breast cancer. Meanwhile, the research method and results of this paper also provide reference and reference for image detection and classification of other malignant tumors.

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.

Prediction and analysis of breast cancer based on transfer learning from DDSM mammography images

Breast cancer is a cancer mostly found in women, causing a high morality rate. It appears as a lump in the breast initially, and as cancer cells spread, the other organs are endangered. Research shows that the earlier the breast cancer is detected, the higher rate of recovering will be. The main methods of breast cancer detection are mainly using various physical methods to probe the internal condition of the patients breast. Breast X-ray imaging, also known as mammography, is the most widely used. It uses low-dose X-ray technology to observe the breast tissue and help doctors figure out the exact condition of the patient. With the advancements in deep learning and image recognition, the analysis of mammograms can be previously done by the computer, easing the pressure on doctors. Digital Database for Screening Mammography (DDSM) dataset is a mammography dataset specifically designed for the development of mammography detection models. It contains huge amounts of mammograms and provides the labels corresponding to them. In this study, the performances of normal deep learning model and transfer learning model on the DDSM dataset are compared. Pretrained neural network models are employed and fine-tuned on the DDSM dataset to adapt to the features of mammographic images. Experimental results demonstrate significant improvements through transfer learning, outperforming traditional methods with Convolutional Neural Network (CNN). Finally, the models are evaluated in multiple aspects, conclusions and prospects are analysed based on the results.

Method of primary breast cancer detection and the disease-free interval, adjusting for lead time.

Little is known about the impact of screen-detected breast cancer compared with clinically detected breast cancer on the disease-free interval (ie, free from locoregional recurrences, distant metastasis, contralateral breast cancer). Moreover, it is thought that most studies overestimate the beneficial effect of screening, as they do not adjust for lead time. We investigated the association between method of breast cancer detection and disease-free interval, taking lead time into account. Women aged 50-76 years, diagnosed with breast cancer between 2005 and 2008 were selected from the Netherlands Cancer Registry. Women diagnosed in 2005 were divided into screen-detected and clinically detected cancer and had a follow-up of 10 years (2005 cohort). Women diagnosed in 2006-2008 were divided into screen-detected, interval, and nonscreen-related cancer and had a follow-up of 5 years (2006-2008 cohort). A previously published method was used to adjust for lead time. Analyses were repeated correcting for confounding variables instead of lead time. The 2005 cohort included 6215 women. Women with screen-detected cancer had an improved disease-free interval compared with women with clinically detected cancer (hazard ratio [HR] = 0.77, 95% confidence interval [CI] = 0.68 to 0.87). The 2006-2008 cohort included 15 176 women. Women with screen-detected or interval cancer had an improved disease-free interval compared with women with nonscreen-related cancer (HR = 0.76, 95% CI = 0.66 to 0.88; HR = 0.88, 95% CI = 0.78 to 0.99, respectively). Correcting for confounders instead of lead time did not change associations. Women with screen-detected cancer had an improved disease-free interval compared with women with a nonscreen-related or clinically detected cancer, after correction for lead time.

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.

Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

Optimizing Drug Delivery Systems for Enhanced Therapeutic Efficacy in Cancer Treatment: A Multidisciplinary Approach

This research aims to develop an early detection method for breast cancer through the analysis of specific biomarkers. The method leverages advanced drug delivery technology to enhance the efficacy of cancer therapy. Employing a multidisciplinary approach, this study integrates medical and pharmaceutical sciences to achieve optimal results. Specific biomarkers utilized in this research will be identified to improve the sensitivity and specificity of cancer detection. The findings of this research are anticipated to make a significant contribution to enhancing early breast cancer detection capabilities and optimizing therapy utilization in patients.

An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images

Breast cancer is the second major reason of death among women around the world. Early and accurate breast cancer detection is important for proper treatment planning to save a life. In this paper, a deep learning-based ensemble classifier is proposed for the detection of breast cancer. The primary contributions are: (1) an efficient deep learning-based breast cancer detection method that can exhibit admirable performance with a small dataset; (2) the integration of three efficient transfer learning models (AlexNet, ResNet, and MobileNetV2), which lead to more accurate results; (3) the use of residual learning, depthwise separable convolution, and inverted residual bottleneck structure to make the system faster, as well as skip connection to make optimization easier and lastly, employing Laplacian of Gaussian (LoG) and modified high-boosting to improve performance. The experimental results convey that the suggested scheme gives superior classification performance by achieving an accuracy of 99.17% to detect abnormality and 97.75% to detect malignancy on the mini-DDSM dataset. Similarly, on the ultrasound dataset (BUSI), it provides accuracies of 96.92% and 94.62% to detect abnormality and malignancy, respectively. It also gives the best performance in another ultrasound dataset, BUS2, with 97.50% accuracy. Therefore, because of its versatility and reliability, the proposed model can be used for breast cancer detection in multimodal datasets.

COVID-19 related change in breast cancer diagnosis, stage, treatment, and case volume: 2019–2021

PurposeEvaluate the COVID-19 pandemic impact on breast cancer detection method, stage and treatment before, during and after health care restrictions.MethodsIn a retrospective tertiary cancer care center cohort, first primary breast cancer (BC) patients, years 2019–2021, were reviewed (n = 1787). Chi-square statistical comparisons of detection method (patient (PtD)/mammography (MamD), Stage (0-IV) and treatment by pre-pandemic time 1: 2019 + Q1 2020; peak-pandemic time 2: Q2-Q4 2020; pandemic time 3: Q1-Q4 2021 (Q = quarter) periods and logistic regression for odds ratios were used.ResultsBC case volume decreased 22% in 2020 (N = 533) (p = .001). MamD declined from 64% pre-pandemic to 58% peak-pandemic, and increased to 71% in 2021 (p < .001). PtD increased from 30 to 36% peak-pandemic and declined to 25% in 2021 (p < .001). Diagnosis of Stage 0/I BC declined peak-pandemic when screening mammography was curtailed due to lock-down mandates but rebounded above pre-pandemic levels in 2021. In adjusted regression, peak-pandemic stage 0/I BC diagnosis decreased 24% (OR = 0.76, 95% CI: 0.60, 0.96, p = .021) and increased 34% in 2021 (OR = 1.34, 95% CI: 1.06, 1.70, p = .014). Peak-pandemic neoadjuvant therapy increased from 33 to 38% (p < .001), primarily for surgical delay cases.ConclusionsThe COVID-19 pandemic restricted health-care access, reduced mammography screening and created surgical delays. During the peak-pandemic time, due to restricted or no access to mammography screening, we observed a decrease in stage 0/I BC by number and proportion. Continued low case numbers represent a need to re-establish screening behavior and staffing.

UPAYA PENINGKATAN SADARI UNTUK DETEKSI DINI KANKER PAYUDARA PADA REMAJA PUTRI

Breast cancer is a disease that threatens women's health, but early detection through the Self-Examination method can increase the chances of recovery. Young women at SMK Cendikia Husada Kotabumi are a group that is susceptible to breast cancer, but awareness of the importance of self-awareness is still low among them. This community service aims to increase the awareness and skills of Sadari in young women at SMK Cendikia Husada Kotabumi to support early detection of breast cancer. The community engagement approach was conducted through socialization, education, and training on the Sadari method. The activity was carried out collaboratively between the community engagement team and the school, involving 27 adolescent girls as participants. The results of the activity showed a significant improvement in the level of knowledge and understanding of the adolescent girls regarding the importance of Sadari as an early detection method for breast cancer, as evidenced by the percentage results before and after the activity. Prior to the activity, the majority of the participants had a low level of knowledge, with 21 individuals (77.8%), while after the activity, the majority of the participants showed good knowledge, with 25 individuals (92.6%). They also successfully developed skills in performing Sadari independently. Community service through outreach, counseling, and training on Realization is effective in increasing awareness and skills of young women in early detection of breast cancer. These steps need to be continued and extended to other schools and communities to protect women's health more broadly

Breast ultrasound image classification and physiological assessment based on GoogLeNet

BackgroundMedical ultrasound image classification based on convolutional neural network is the mainstream breast cancer classification model, but its limited perceptual ability limits its ability to obtain global information. MethodologyA total of 880 breast ultrasound images were collected from 700 patients including 103 normal images, 467 malignant tumor images and 210 benign tumor images. In this paper, the diagnosis of breast ultrasound images was realized by constructing the CNN model of GoogLeNet. Firstly, the breast images were preprocessed based on the TV model. After that, the CNN model was trained and a more accurate model with a wider range of application was obtained based on improved Inception. Then we extract image features of different sizes; Then, feature classification was completed in different model classifiers to realize benign and malignant detection of breast cancer. Meanwhile, comparative analysis was performed to verify the excellence of the GoogLeNet model. ResultsThe training time for classification was effectively reduced, and the classification accuracy rate was improved, reaching 96.37% combined with transformer learning. The loss value is down to 0.3492. Then, the classification accuracy of different structural models is discussed in the two models. The results show that GoogLeNet has great advantages in the detection of breast cancer ultrasound images. The influence of migration on experimental results is further discussed. Finally, combined with transfer learning, the three models were tested separately. The results show that transfer learning can improve system performance. ConclusionBased on the TV model and the GoogLeNet model, this paper designs a method of breast cancer detection and classification based on the combination of TV and GoogLeNet model and transfer learning. Experiments show that this method can repair part of the texture damaged by markers in ultrasonic images, effectively extract the features of thyroid nodule, and accurately judge whether the breast is diseased, which greatly improves the diagnostic efficiency of doctors.

Beyond a decade: a comparative study of 15-year survival rates in screen-detected vs. symptomatic breast cancer patients in Hungary

BackgroundBreast cancer is the leading cancer in women globally. Despite decreasing mortality rates, largely due to early detection and modern treatment, the effectiveness of screening on long-term survival outcomes remains unclear.AimsThis study evaluates the 15-year survival outcomes of a national breast cancer screening program initiated in Hungary in 2002.MethodsUsing a prospectively maintained patient database, the study included individuals from the first 6 years of the program who underwent surgery for histologically confirmed breast cancer and had available follow-up information. Patients were categorized based on the method of breast cancer detection into two groups: those diagnosed during or 2 years after the population-based screening exam (Group A), and those who self-detected or sought medical attention for symptoms (Group B).ResultsOf the 309 patients who underwent breast cancer surgery, 208 were screen-detected (Group A) and 101 were symptomatic (Group B). The 15-year overall survival was 75.0% for Group A and 76.2% for Group B (p = 0.927). The 15-year disease-specific survival was 85.6% and 81.2% (p = 0.249), respectively. A statistically not significant positive trend in disease-free survival was observed in Group A (81.7% vs. 75.2%; p = 0.144).ConclusionsThe study underscores the importance of extended follow-up periods in evaluating the outcomes of breast cancer screening programs. While the screening program may not significantly enhance overall survival rates, it has demonstrated a reduction in the mastectomy rate and could potentially extend periods of disease-free survival. These findings contribute to the ongoing discourse about the long-term benefits of breast cancer screening programs.

Awareness and Practice of Breast Self-Examination among Female Undergraduate Students

Breast cancer is a prevalent form of cancer and the second leading cause of cancer-related deaths in women across the globe. Women can perform breast self-examination at home to detect any abnormalities in the breast early on, making it an affordable, easy, and effective screening technique. In this cohort study, we aimed to assess undergraduates' knowledge and practice of breast self-examination. Between February 2023 and May 2023, we used an online survey tool to collect information and structured the questions to meet our research objectives. Findings indicate that 40% of those recruited were knowledgeable about the procedure, while 60% had minimal or no knowledge. Age did not significantly impact knowledge and practice (p=0.7). Women, especially female students, must be more informed about breast cancer and encouraged to perform breast self-examination regularly. Raising awareness can be achieved through organizing workshops on breast cancer and early detection methods on university campuses, secondary schools, and even in public markets. Pamphlets may also be an effective method for promoting awareness.

Classifications of benign and malignant mammogram images using Gabor‐modified CNN architecture

AbstractBreast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. Retrospective. A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T. A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively. Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant. With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively. The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity. 3. Stage 2.

Review of Breast Cancer Detection Method

The fierceness of breast cancer is obvious for the patients and the field of medical studies for its malignant destruction in the human body. The failure in diagnosis may put patients in suffering from high financial pressure and may cause the patients’ physical health to deteriorate. The key in this research of contrasting different kinds of algorithms and varied types of classifications is to support physicians to make a better judgment to prevent misdiagnosis. The research introduced many aspects of data on breast cancer detection to make the data inputs of algorithms able to satisfy doctors’ needs. Moreover, the varied kinds of features will specify and confirm the presence of breast cancer tumours instead of treating them as a part of the benign tissues. With the information above, the research found a difference in accuracy between the classification and tried to make a comparison to conclude that most satisfy and support the actual use of these data to physicians, who may see the symptoms with misunderstanding. Thus, Random Forest was qualified to be evaluated as the most applicable algorithm of its excellence in processing higher dimensional, which is dynamic, data.

Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.