MobileNetV2 Architecture Research Articles

Detection of Defects on Metal Surfaces Based on Deep Learning

Various types of defects can occur on metal surfaces during production due to various factors. Detecting these defects is of great importance for the quality and reliability of the product. Manual inspections are time-consuming and prone to errors, especially as production scales increase Although Deep Learning and Computer Vision techniques show promise, the large data sizes of datasets created for deep learning and the high training costs of deep learning models lead to negative effects in terms of storage and energy efficiency. We propose a new method to improve defect detection rates, reduce labor losses, decrease data sizes, and improve energy efficiency. For the experiments, we used the Northeastern University (NEU) surface defect database as a reference, and the MobileNetV2 architecture as the model. The deep learning model was trained separately using both the grayscale-converted NEU database and the database created with the proposed method, and the accuracies of the datasets were compared. Image preprocessing techniques such as morphological operations, Gaussian noise addition, Principal Component Analysis, and image thresholding with Otsu thresholding were used for defect detection. The model was trained using the newly created database, achieving a successful result with an average accuracy rate of 87% using the proposed algorithm.

Defect attention-based lightweight real-time adaptable surface defect analysis

Surface defect diagnosis for steel sheets is essential to maintain the quality of the product. However, it is challenging to do such a task due to the high manufacturing speed of 100–140 m/min, making it impossible to observe defects visually. However, developing a lightweight and real-time adaptable intelligent approach can both overcome this issue and satisfy the requirements of the concept of Industry 4.0. Based on that motivation, this study aimed to satisfy such a demand by introducing an attention-based surface defect identification approach called MobileNetV2-AGCA and UNET-AGCA for defect localisation. The effectiveness of the proposed techniques was evaluated by considering steel strip surface defect images of the NEU-CLS and NEU-DET surface defect dataset where six different defects are included. The Adaptive Graph Channel Attention (AGCA) Module was implemented in an edited version of MobileNetV2 and UNET architecture to improve segmentation accuracy while maintaining or improving the execution speed. According to the experimental results, MobileNetV2-AGCA successfully identified the surface defects with an average accuracy of 99.72%. Regarding defect localisation, UNET-AGCA reached an F1 score of 0.795. It is concluded that the proposed methods provided promising results to be applied to a real-time problem related to surface defect identification and localisation.

Enhancing Epstein–Barr virus detection in IBD patients with XAI and clinical data integration

BackgroundThere is increasing evidence of a link between Epstein–Barr virus (EBV) and gastrointestinal cancers, particularly in immunocompromised patients and those on thiopurines. Traditional endoscopic techniques have limitations in diagnosing EBV, whereas standard methods for diagnosing EBV have practical challenges in regular clinical settings related to their invasiveness and high costs. MethodsAn explainable AI (XAI) model was developed to analyze 1598 endoscopic images from 287 patients with inflammatory bowel disease (IBD), with a focus on detecting EBV infection. Following the application of data augmentation and transfer learning techniques, the model accurately classified the presence of EBV, and its performance was quantified through receiver operating characteristic (ROC) curve analysis, including calculation of the area under the curve (AUC). Furthermore, a combination of the Grad-CAM method and clinical data analysis significantly improved the interpretability and diagnostic accuracy of the model. ResultsOur model significantly outperformed other models, including those developed with ResNet50, Vision Transformer (ViT), and MobileNet v2 architectures, in detecting EBV on endoscopic images, achieving an accuracy of 73.83 % and an F1-score of 73.70 %. The model showed good performance in distinguishing between EBV-positive and EBV-negative images according to the confusion matrix and ROC curve analysis, with metrics including a true negative rate of 79.76 %, a true positive rate of 67.32 %, and an AUC of 0.74. Additionally, the generated salience maps effectively identified key regions in the images, enhancing lesion detection in patients with EBV infection. Our correlational analysis revealed significant associations between EBV infection and clinical parameters such as age, illness duration, and total bilirubin, and suggested that EBV infection had a notably greater incidence in ulcerative colitis (UC) patients than in Crohn's disease (CD) patients. ConclusionOur study successfully created an XAI-assisted system that allows the accurate detection of EBV in endoscopic images and improves the diagnosis of EBV infection through the integration of clinical data.

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

This study introduces significant improvements in the construction of deep convolutional neural network models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as depthwise separable convolutions, linear bottlenecks, and inverted residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 99.53%∼100% with nearly perfect precision, recall of 95.7%, and F1-score of 94.6% for both “orange_good” and “orange_bad” classes, significantly outperforming previous models which typically achieved accuracies between 70% and 90%. While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study highlights the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. These findings affirm the model in this research as a reliable and highly efficient tool for agricultural product classification, surpassing the capabilities of traditional models in this field.

Klasifikasi Jenis Jerawat Berdasarkan Citra Menggunakan Convolutional Neural Network dengan Arsitektur MobileNetV2

Among teenagers and adults worldwide, acne ranks as one of the most frequent skin concerns where each type of acne requires specific treatments. The proposed method in this study classifies five types of acne using deep learning. This methodology utilizes CNN architecture to specifically categorize different types of acne. The model used in this research adopts the MobileNetV2 architecture with transfer learning, consisting of several main layers: MobileNetV2 as the base model, Global Average Pooling, Flatten, Dense Layer, Dropout, and Softmax classification. The validation of the architecture utilizes secondary data consisting of 350 images. This dataset covers five different types of acne: Acne Fulminans, Acne Nodules, Papule, Pustule, and Fungal Acne, with each class containing 70 different samples. Challenges in automating this process include image lighting variations, image quality and data limitations. The CNN classification system shows performance rates of 89% for training and 80% for testing. The results indicatee that the proposed architecture can classify acne types with a reasonably good accuracy level, although there is room for improvement in model generalization.

ADVANCED SKIN CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING

This study investigates the effectiveness of MobileNetV2 transfer learning method and a deep learning based Convolutional Neural Network (CNN) model for the classification of malignant and benign skin lesions in skin cancer diagnosis. Since skin cancer is a disease that can be cured with early detection but can be fatal if delayed, accurate diagnosis is of great importance. The model was trained with MobileNetV2 architecture and performed the classification task with high accuracy on images of skin lesions. Metrics such as accuracy, recall, precision and F1 score obtained during the training and validation processes support the high performance of the model. The accuracy of the model was 92.97%, Recall 92.71%, Precision 94.70% and F1 score 93.47%. The results show that the CNN-based MobileNetV2 model is a reliable and effective tool for skin cancer diagnosis, but small fluctuations in the validation phase require further data and hyperparameter optimization to further improve the generalization ability of the model. This study demonstrates that CNN-based models enhanced with MobileNetV2 transfer learning offer a powerful solution to medical image classification problems and have the potential to contribute to the development of early detection systems in the healthcare field.

A Comparison of Convolutional Neural Network (CNN) and Transfer Learning MobileNetV2 Performance on Spices Images Classification

This research was conducted to analyze the performance of the CNN algorithm without transfer learning in classifying spice images and compare it with the CNN algorithm using transfer learning on the MobileNetV2 architecture. This comparison aims to evaluate both methods' accuracy, efficiency, and overall performance and analyze the impact of transfer learning on classification results in the context of spices. The dataset consists of 1500 spice images divided into 10 classes, with each class of 150 images. In the first experiment, CNN without transfer learning resulted in 93% accuracy performance. For the second experiment using MobileNetV2, there was an increase in accuracy, reaching a value of 99% for all spice classes. The results of this study confirm that MobileNetV2 architecture significantly improves the accuracy and performance of spice classification compared to CNN without transfer learning, which can be recommended for spice image classification.

Identification of Cactus Species Using the MobileNet Convolutional Neural Network Model

The objective of this design is to create a system capable of accurately and precisely classifying different types of succulent cacti. The system design aims to create a succulent cactus classification application using a Convolutional Neural Network (CNN) with the MobileNet architecture. The process involves collecting cactus images, dividing them into training and test datasets, and developing a CNN model to recognize patterns in succulent cactus species. The training data is used to train the CNN model, and the test data evaluates the model's accuracy. The trained model, stored in Tf.lite format, successfully classifies 15 cactus types, achieving high accuracy by employing preprocessing steps like resizing, normalization, and background removal. Over 1,200 cactus images were taken with a smartphone, categorized into 15 classes, and prepared to ensure optimal lighting, angle, background, and resolution (224x224 pixels). The MobileNet model was chosen for its high accuracy and efficiency. Hardware used includes a Samsung A54 smartphone and an Intel i7 laptop, with software such as Python, Kotlin, and Android Studio facilitating development. This design ensures the application’s accessibility, making it a valuable tool for cactus enthusiasts and the general public to easily identify different succulent cactus types. Testing of the cactus species classification program using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture yielded strong results, achieving over 90% accuracy in classifying 15 cactus species. The highest training accuracy of 0.9837 was achieved at 150 epochs without early stopping, outperforming other epoch configurations. The model successfully classified species across five main genera—Kalanchoe, Crassula, Echeveria, Haworthia, and Euphorbia. This high accuracy highlights the model's effectiveness, making it a useful tool for cactus enthusiasts and the public to accurately identify and distinguish cactus species.

Segmentation and classification of white blood SMEAR images using modified CNN architecture

The classification and recognition of leukocytes or WBCs in blood smear images presents a key role in the corresponding diagnosis of specific diseases, such as leukemia, tumor, hematological disorders, etc. The computerized framework for automated segmentation & classification of WBCs nucleus contributes an important role for the recognition of WBCs related disorders. Therefore, this work emphasizes WBCs nucleus segmentation using modified U-Net architecture and the segmented WBCs nucleus are further classified into their subcategory i.e., basophil, eosinophil, neutrophil, monocyte and lymphocyte. The classification and nucleus characterization task has been performed using VGGNet and MobileNet V2 architecture. Initially, collected instances are passed to the preprocessing phase for image rescaling and normalization. The rescaled and normalized instances are passed to the U-Net model for nucleus segmentation. Extracted nucleus are forwarded to the classification phase for their class identifications. Furthermore, the functioning of the intended design will be compared with other modern methods. By the end of this study a successful model classifying various nucleus morphologies such as Basophil, Eosinophil, Lymphocyte, Monocyte and Neutrophil was obtained where overall test accuracy achieved was 97.0% for VGGNet classifier and 94.0% for MobileNet V2 classifier.

Mobile Application Based Indoor Routing System Using Transfer Learning

Nowadays, indoor routing in places with complex multi-storey architecture such as hospitals, shopping malls, parking garages and public buildings is traditionally carried out using signage or devices in a fixed position. When we examine the literature, it is generally seen that indoor orientation studies for certain needs are seen. The fact that the routing systems are fixed, and the signage is not an effective tool constitutes the motivation of this study. In this study, an image-based mobile application that is hardware-independent and adaptable to other interior spaces has been implemented using a mobile device. The application basically consists of two parts. In the first part, transfer learning based MobileNetV2 architecture is used to determine the initial store location. The proposed model detects the store signage image taken from the camera with 96% success. In the second part, the user is successfully guided to the target using the Dijkstra algorithm. With the developed mobile application, the user can reach the targets on the same or different floors in the fastest way without wasting time and without asking anyone. The application was tried in real time in a shopping center and successful results are obtained.

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps.

The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours, the variety of sticky trap colours poses a challenge to the performance of the models. However, the effectiveness of deep learning in classifying pests on different coloured sticky traps has not yet been sufficiently explored. In this study, we aim to investigate the influence of sticky trap colour and imaging devices on the performance of deep learning models in classifying pests on sticky traps. Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. Using a generalised linear model (GLM) and a Boruta feature selection algorithm, we also showed that the colour and architecture of the sticky traps significantly influenced the performance of the model. Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices. Various input combinations using edge maps, thresholding, and hard attention versions were explored. The effect of augmenting the three-channel inputs of pre-trained models on classification performance was analyzed. ROC analyses and confusion matrix-derived performance metrics of the models were reported. Of the 271 patients included in this study, 151 (55.7%) were male and 120 (44.3%) were female. One hundred twenty-nine patients had MCA (47.6%), 65 patients had posterior circulation (24%), and 77 patients had watershed (28.0%) infarcts for center 1. Of the 122 patients from center 2, 78 (64%) were male and 44 (34%) were female. Fifty-two patients (43%) had MCA, 51 patients had posterior circulation (42%), and 19 (15%) patients had watershed infarcts. The Mobile-Crop model had the best performance with 0.95 accuracy and a 0.91 mean f1 score for slice-wise classification and 0.88 accuracy on external test sets, along with a 0.92 mean AUC. In conclusion, modified pre-trained models may be augmented with the transformation of images to provide a more accurate classification of affected territory by stroke in DWI.

Classification of Kinalabasa Tomato Using Convolutional Neural Network

Determining the freshness of tomatoes is essential for evaluating their quality, impacting both consumer satisfaction and the economic benefits for farmers. Freshness is typically assessed by outer appearance, including color, size, and shape, with skin color indicating ripeness and influencing selling price. Properly assessing freshness also helps determine the shelf life of stored tomatoes. This study introduced an affordable and straightforward technique for classifying agricultural commodities using image processing, focusing on Kinalabasa tomatoes and classifying them based on color features into three categories. The study employed the MobileNetv2 architecture for training and testing the dataset, aiming to improve the classification of Kinalabasa tomatoes through deep learning techniques. The study evaluated the model's performance using metrics like accuracy, precision, sensitivity, and specificity. The optimal configuration for classifying Kinalabasa tomatoes was determined to be 10 epochs and a learning rate of 0.001. MobileNetv2 achieved a high accuracy rate of 94%, demonstrating its effectiveness in classifying tomatoes, though the specificity rate of 64% indicated some room for improvement in identifying negative instances. Comparing MobileNetv2 with ShuffleNet architectures will provide further insights into their respective effectiveness and performance in this classification task.

An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.

Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model's performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells.

Unlocking Early Detection and Intervention Potential: Analyzing Visual Evoked Potentials (VEPs) in Adolescents/Teenagers with Narcotics Abuse Tendencies from the TelUnisba Neuropsychology EEG Dataset (TUNDA)

Narcotics abuse has extensive negative impacts on individuals, families, and society, including physical harm to organs and mental health disorders. Addressing teenage narcotics problems requires collaborative efforts involving educational institutions, families, and psychologists. Currently, narcotics has increasingly targeted teenagers, becoming a serious issue that demands special attention in prevention and treatment. Handling narcotic problems at the adolescent level necessitates close collaboration among educational institutions, families, and the community, including psychologists. Emphasizing the importance of early detection and prevention, this study proposes a method to detect the possibility of narcotic abuse in adolescents using the Go/No-Go Association Task (GNAT) test designed by psychologists. The study introduced the TelUnisba Neuropsychology EEG Dataset (TUNDA), an open EEG dataset with data on the emotional and habitual aspects of drug abuse in Indonesia, classified into "normal" and "risk" by psychologists. The processed EEG signal is the visual evoked potential (VEP) within 1000 milliseconds following the visual stimulus onset. The data is classified as “slow” and “fast” based on respondent's responses using MobileNetV2 architecture. Results showed MobileNetV2 achieved the highest accuracy for both normal and risk categories, with accuracies of 0.86 and 0.85 respectively. This study obtained ethical clearance and received funding support from Telkom University and Universitas Islam Bandung, with technical assistance from the Smart Data Sensing Laboratory. The authors declare no conflicts of interest related to this study.

Advanced Gallbladder Segmentation in Dynamic Ultrasound Imaging Using Fully Convolutional Networks

This study develops an advanced technique for segmenting the gallbladder from dynamic B-mode ultrasound images to enhance the accuracy and efficiency of volumetric analysis in medical diagnostics. Using a Wi-Fi probe, volumetric data are captured and processed into labeled images for training a fully convolutional network (FCN) model with specifications including an epoch of 9, a batch size of 3, and a learning rate of 0.001. Performance metrics such as global accuracy, mean accuracy, and Intersection over Union (IoU) are evaluated. The MobileNetV2 architecture achieves a maximum mean IoU of 0.690 and a mean Boundary F1 (BF) score of 0.990, while the ResNet50 architecture demonstrates significant effectiveness. This study substantiates the effectiveness of the MobileNetV2 architecture for precise gallbladder segmentation in dynamic B-mode ultrasound imaging.

Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset’s images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm does not influence the effectiveness of treatment or recovery from the condition.

Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2

Paddy farming, a cornerstone of global agriculture, faces significant threats from various diseases that affect the crop yield. This research presents a novel approach for detecting paddy leaf diseases using advanced deep learning techniques, specifically transfer learning with the MobileNetV2 architecture. The methodology involves the utilization of a comprehensive dataset consisting of paddy leaf images across multiple disease classes. Data augmentation was extensively employed to address the limitations posed by the dataset size. Both basic and advanced models were trained, with the advanced model achieving a remarkable validation accuracy of 97%. Additionally, Time-Test Augmentation (TTA) was applied to further enhance the model's performance. This research demonstrates the efficacy of deep learning techniques in agricultural disease detection and highlights potential improvements for future applications.

Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images

Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in the medical sector. It includes detecting and analysing abnormal or cancerous tissue from the larynx, an integral part of the vocal and respiratory systems. The computer-aided system makes use of artificial intelligence (AI) through deep learning (DL) and machine learning (ML) models, including convolution neural networks (CNN), for automated disease diagnoses and detection. Various DL and ML approaches are executed to categorize the extraction feature as healthy and cancerous tissues. This article introduces an automated Laryngeal Cancer Diagnosis using the Dandelion Optimizer Algorithm with Ensemble Learning (LCD-DOAEL) method on Biomedical Throat Region Image. The LCD-DOAEL method aims to investigate the images of the throat region for the presence of laryngeal cancer. In the LCD-DOAEL method, the Gaussian filtering (GF) approach is applied to eliminate the noise in the biomedical images. Besides, the complex and intrinsic feature patterns can be extracted by the MobileNetv2 model. Meanwhile, the DOA model carries out the hyperparameter selection of MobileNetV2 architecture. Finally, the ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), regularized extreme learning machine (ELM), and backpropagation neural network (BPNN) models, are utilized for the classification process. A comprehensive set of simulations is conducted on the biomedical image dataset to highlight the efficient performance of the LCD-DOAEL technique. The comparison analysis of the LCD-DOAEL method exhibited a superior accuracy outcome of 97.54% over other existing techniques.

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.