An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN. In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning. Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94 percent, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90 percent, whereas ResNet- 50 reached 86 percent and Inception v3 obtained 64 percent accuracy. When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and reduction in losses.

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

Indonesia is home to over 30,000 types of herbal plants, with approximately 1,200 species utilized as raw materials for alternative and traditional medicine. Leaves play a crucial role in herbal medicine preparation. However, many people struggle to identify different herbal leaves due to their similar appearances, making classification difficult. Each leaf possesses unique characteristics such as shape, size, midrib, stalk, blade, and type, which can be used for differentiation. To assist in identifying herbal leaves, a classification system based on image recognition is essential. Convolutional Neural Networks (CNN) are deep learning algorithms designed for processing two-dimensional image data. Model performance can be enhanced through transfer learning, with MobileNetV2 and ResNet50V2 being widely used architectures. These pretrained models have been trained to recognize images with high accuracy. This study focuses on classifying herbal plants based on leaf shape using CNN architectures from MobileNetV2 and ResNet50V2. The evaluation results show that the MobileNetV2 architecture, with a 90%:10% data split, achieved an accuracy of 98.51%, precision of 98.92%, recall of 98.51%, and an F1-score of 98.56%. These findings indicate that CNN with transfer learning can effectively classify herbal leaves with high accuracy.

Early detection of brain tumors improves treatment options and increases the patient’s survival percentage. Tumor cells are exceedingly difficult to detect due to their diversity. Manual brain tumors detection for cancer diagnosis from a large number of MRI images obtained in clinical practice is a challenging and time-consuming task. The primary goal of this study is to investigate the ability of various pre-trained DCNN models using transfer learning to detect diseased brain images. This work focuses on constructing an effective and reliable method for detecting brain tumors using MRI to aid neurologists in making quick and appropriate decisions. The framework, which includes a dataset of 3064 images has been created and applied for the identification of brain tumors employing deep learning techniques. We have used conceptual model with Transfer Learning (TL) to verify it and got 98.28% accuracy, 97.51% recall and 97.43% precision. The proposed TL based brain tumor detection algorithm outperforms existing pre-trained models.

Deep neural networks such as Convolutional Neural Networks (CNNs) have achieved several significant milestones in visual data analytics. Benefited from transfer learning, many researchers use pre-trained CNN models to accelerate the training process. However, there is still uncertainty about the deep learning models, structures, and applications. For instance, the diversity of the datasets may affect the performance of each pre-trained model. Therefore, in this paper, we proposed a new approach based on genetic algorithms to select or regenerate the best pre-trained CNN models for different visual datasets. A new genetic encoding model is presented which denotes different pre-trained models in our population. During the evolutionary process, the optimal genetic code that represents the best model is selected, or new competitive individuals are generated using the genetic operations. The experimental results illustrate the effectiveness of the proposed framework which outperforms several existing approaches in visual data classification.

—Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

Early diagnosis of brain tumor plays an important factor in extending the life expectancy of a patient. Therefore, an accurate and timely diagnosis of the type of brain tumor will allow adequate treatment planning and medical assistance. Radiologists commonly use magnetic resonance imaging (MRI) scans to detect and classify brain tumors. The current methods used in the medical field for diagnosis are time-consuming and prone to human error. In recent years, researchers have developed automated techniques for the segmentation and classification of MRI images resulting in a faster diagnosis process. Recent advancements in deep learning have shown greater efficiency in image recognition and classification tasks. In this paper, a convolutional neural network (CNN) (a widely used deep learning architecture for image classification tasks) is developed to classify MRI images into four brain tumor categories. Data augmentation is applied to the training dataset to generalize the images and avoid overfitting problem. Additionally, this paper compares the performance of various pre-trained models such as Vision Transformer (VIT), VGG19, ResNet50, Inception V3, and AlexNet50 with that of the proposed model. Each experiment then explores transfer learning techniques like fine-tuning and freezing layers. In the study, the proposed model yields the most efficient results with a classification accuracy of 94.72%.

Brain tumors are among the most common and fatal types of cancer.Accurate and timely diagnosis of these tumors is essential for disease management and successful patient prognosis.Additionally, the precise identification of the tumor type is crucial in determining the treatment path.By recognizing the type of tumor, doctors can select the most appropriate treatment method, which may include surgery, radiation therapy, chemotherapy, or a combination of these methods.Furthermore, the tumor type helps predict disease progression and the quality of life post-treatment.In recent years, deep learning has been a powerful tool for various image-processing tasks, including brain tumor detection.In this paper, various deep learning models such as CNN, RNN, VGG16, InceptionV3, and ResNet101 are evaluated for classifying brain tumor types from the Figshare dataset based on MRI images of glioma, meningioma, pituitary tumors, and no tumor.Finally, a suitable deep model based on ResNet101 combined with transfer learning is proposed.Based on various metrics and statistical tests, the findings indicate that deep learning models can be effectively used for brain tumor detection.Among these, the ResNet101 model achieved an accuracy of 98.37% in classifying the four tumor classes.This study demonstrates that deep learning holds significant potential for improving brain tumor detection accuracy.

In this study, an application of Manta Ray Foraging Optimization (MRFO) algorithm is demonstrated for PV parameter extraction. The MRFO is utilized to extract the parameters of both single diode and double diode model R.T.C. France silicon solar cell. The algorithm is easy to implement with less computational time. The MRFO optimizer is compared to Artificial Bee Optimization (ABO) and Flower Pollination Algorithms (FPA) algorithms. The results demonstrated the ability of MRFO for parameter extraction.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

Quality assurance of data prior to use in automated pipelines and image analysis would assist in safeguarding against biases and incorrect interpretation of results. Automation of quality assurance steps would further improve robustness and efficiency of these methods, motivating widespread adoption of techniques. Previous work by our group demonstrated the ability of convolutional neural networks (CNN) to efficiently classify head and neck (H&N) computed-tomography (CT) images for the presence of dental artifacts (DA) that obscure visualization of structures and the accuracy of Hounsfield units. In this work we demonstrate the generalizability of our previous methodology by validating CNNs on six external datasets, and the potential benefits of transfer learning with fine-tuning on CNN performance. 2112 H&N CT images from seven institutions were scored as DA positive or negative. 1538 images from a single institution were used to train three CNNs with resampling grid sizes of 643, 1283 and 2563. The remaining six external datasets were used in five-fold cross-validation with a data split of 20% training/fine-tuning and 80% validation. The three pre-trained models were each validated using the five-folds of the six external datasets. The pre-trained models also underwent transfer learning with fine-tuning using the 20% training/fine-tuning data, and validated using the corresponding validation datasets. The highest micro-averaged AUC for our pre-trained models across all external datasets occurred with a resampling grid of 2563 (AUC = 0.91 ± 0.01). Transfer learning with fine-tuning improved generalizability when utilizing a resampling grid of 2563 to a micro-averaged AUC of 0.92 ± 0.01. Despite these promising results, transfer learning did not improve AUC when utilizing small resampling grids or small datasets. Our work demonstrates the potential of our previously developed automated quality assurance methods to generalize to external datasets. Additionally, we showed that transfer learning with fine-tuning using small portions of external datasets can be used to fine-tune models for improved performance when large variations in images are present.

Coffee bean production can encounter challenges due to fluctuations in global coffee prices, impacting the economic stability of some countries that heavily depend on coffee production. The primary objective is to evaluate how effectively various pre-trained models can predict coffee types using advanced deep learning techniques. The selection of an optimal pre-trained model is crucial, given the growing popularity of specialty coffee and the necessity for precise classification. We conducted a comprehensive comparison of several pre-trained models, including AlexNet, LeNet, HRNet, Google Net, Mobile V2 Net, ResNet (50), VGG, Efficient, Darknet, and DenseNet, utilizing a coffee-type dataset. By leveraging transfer learning and fine-tuning, we assess the generalization capabilities of the models for the coffee classification task. Our findings emphasize the substantial impact of the pre-trained model choice on the model's performance, with certain models demonstrating higher accuracy and faster convergence than conventional alternatives. This study offers a thorough evaluation of pre-trained architectural models regarding their effectiveness in coffee classification. Through the evaluation of result metrics, including sensitivity (1.0000), specificity (0.9917), precision (0.9924), negative predictive value (1.0000), accuracy (1.0000), and F1 score (0.9962), our analysis provides nuanced insights into the intricate landscape of pre-trained models.

Brain tumors are abnormal tissue growth characterized by uncontrolled and rapid cell proliferation. Early detection of brain tumors is critical for improving patient outcomes, and magnetic resonance imaging (MRI) has become the most widely used modality for diagnosis due to its superior image quality and non-invasive nature. Deep learning, a subset of artificial intelligence, has revolutionized automated medical image analysis by enabling highly accurate and efficient classification tasks. The objective of this study is to develop a robust and effective brain tumor detection system using MRI images through transfer learning. A diagnostic framework is constructed based on convolutional neural networks (CNN), integrating both a custom sequential CNN model and pretrained architectures, namely VGG16 and EfficientNetB4, trained on the ImageNet dataset. Prior to model training, image preprocessing techniques are applied to enhance feature extraction and overall model performance. This research addresses the common challenge of limited MRI datasets by combining EfficientNetB4 with targeted preprocessing, data augmentation, and an appropriate optimizer selection strategy. The proposed methodology significantly reduces overfitting, improves classification accuracy on small datasets, and remains computationally efficient. Unlike previous studies that focus solely on CNN or VGG16 architectures, this work systematically compares multiple transfer learning models and demonstrates the superiority of EfficientNetB4. Experimental results on the Br35H dataset show that EfficientNetB4, combined with the ADAM optimizer, achieves outstanding performance with an accuracy of 99.66%, precision of 99.68%, and an F1-score of 100%. The findings confirm that integrating EfficientNetB4 with dataset-specific preprocessing and transfer learning provides a highly accurate and cost-effective solution for brain tumor classification, facilitating rapid and reliable medical diagnosis.

Comparative analysis of deep convolution neural network models on small scale datasets