Comparison Of Convolutional Neural Networks Research Articles

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.

Comparison of CNN and ResNet Neural Networks on the Performance of Facial Expression Recognition

Facial expression recognition is a crucial task in numerous applications, including human-computer interaction, mental health monitoring, and human behavior analysis. Previous studies have primarily focused on individual models or techniques for improving emotion classification accuracy. However, a comparative analysis of different neural network architectures' performance for facial expression recognition is lacking. The main objective of this study is to compare the performance of Convolutional Neural Network (CNN) and Residual Network (ResNet) on the Fer2013 dataset. The author aims to analyze their behavior during the initial training phases and identify the architectural advantages and challenges associated with each model. Both models are trained using the same experimental setup to ensure a fair comparison. The models are trained using the Fer2013 dataset. The author employs a standard protocol for data preprocessing and augmentation. Results show that CNN achieves an accuracy of around 0.5 in the initial stages of training, which is significantly higher than ResNet's accuracy of 0.25. However, as training progresses, ResNet may outperform CNN because it has a more complicated structure that can capture more complex patterns. CNN exhibits superior performance during the initial training stage of the Fer2013 dataset. This reason may lie behind the fact that CNN has a simpler structure which makes it more sensitive on the basic features of the data.

Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.

Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process

Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Abstract: This paper delves into the exploration and comparison of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model for the task of tomato leaf disease detection. Through rigorous experimentation and evaluation, it was determined that the CNN-LSTM hybrid model demonstrates superior performance both CNN and LSTM individually in terms of accuracy and robustness. Leveraging a comprehensive dataset comprising images of healthy and diseased tomato leaves, including various disease types, such as Bacterial Spot, Early Blight, Septorial Leaf Spot, Leaf Mold, and Yellow Leaf Curl Virus, the study demonistrate the efficacy of the suggested hybrid approach. The hybrid model capitalizes on the strengths of both CNN and LSTM, integrating spatial and temporal information for enhanced disease identification accuracy. The results highlight the effect of hybrid deep learning structures in agricultural disease management, offering a promising solution for early detection and mitigation of plant diseases. This paper dds to the expanding collection of literature on deep learning applications in agriculture and offers valuable insights for researchers and practitioners seeking to address challenges in crop health monitoring and management.

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Comparison of Convolutional Neural Networks and Random Forest Classifiers for Strong Gravitational Lens Identification

Strong gravitational lenses have been instrumental in providing insight into various astronomical problems, including analyzing the dark matter distribution of the universe. Effective identification of these events is made possible through machine learning algorithms, with many recent studies focusing on neural networks. However, very few have investigated the tradeoffs between different algorithms besides neural networks for lens identification. Our paper compares a convolutional neural network (CNN) and a random forest classifier (RFC) to determine the benefits of each for this task. We find that while CNNs do achieve higher accuracy, using RFCs to supplement them could increase the effectiveness of such algorithms. As a result, models that utilize both side-by-side to make predictions may increase in accuracy. This should be explored by future research.

Application of deep learning for semantic segmentation in robotic prostatectomy: Comparison of convolutional neural networks and visual transformers.

Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery. Intraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet, and U-Net++) and three transformers (SegFormer, BEiT, and DPT), and their performances were analyzed. The overall segmentation performance during RARP varied across different model architectures. For the CNN models, DeepLabV3 achieved a mean Dice score of 0.938, MANet scored 0.944, and U-Net++ reached 0.930. For the transformer architectures, SegFormer attained a mean Dice score of 0.919, BEiT scored 0.916, and DPT achieved 0.940. The performance of CNN models was superior to that of transformer models in segmenting the prostate, vas, and seminal vesicle. Deep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

Performance Evaluation of CNN Models for Alzheimer’s Disease Detection with MRI Scans

The primary symptom of Alzheimer's disease is memory impairment, which is a neurodegenerative condition. The manifestation of these symptoms can be attributed to the impairment of the cerebral nerve responsible for cognitive functions such as learning, thinking and memory. Alzheimer’s disease is a prominent cause of mortality and lacks a definitive curve. However, appropriate medicinal interventions have demonstrated the potential to mitigate the progression and severity of the condition. This study presents the comparison of Convolutional Neural Network (CNN) models, namely ResNet50, VGG19 and VGG16 architectures, as an approach to construct an automated classification system for Alzheimer’s disease in future. The study utilises Magnetic Resonance Imaging (MRI) datasets to identify MRI datasets of individual with Alzheimer’s disease (AD), Cognitively normal (CN), mild cognitive impairment (MCI), early mild cognitive impairment (EMCI), and late mild cognitive impairment (LMCI). In the conducted experiment, the study achieved accuracy rates of 91.18% and 94.56% while utilising an epoch size of 2. The accuracy results indicate that the VGG16 model outperforms the ResNet50 model. The utilisation of automated Alzheimer’s disease classification holds potential as an auxiliary tool for healthcare professionals in determining the stage of Alzheimer’s disease hence facilitating the administration of suitable medicinal interventions.

Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest.

Speech is a direct and rich way of transmitting information and emotions from one point to another. In this study, we aimed to classify different emotions in speech using various audio features and machine learning models. We extracted various types of audio features such as Mel-frequency cepstral coefficients, chromogram, Mel-scale spectrogram, spectral contrast feature, Tonnetz representation and zero-crossing rate. We used a limited dataset of speech emotion recognition (SER) and augmented it with additional audios. In addition, In contrast to many previous studies, we combined all audio files together before conducting our analysis. We compared the performance of two models: one-dimensional convolutional neural network (conv1D) and random forest (RF), with RF-based feature selection. Our results showed that RF with feature selection achieved higher average accuracy (69%) than conv1D and had the highest precision for fear (72%) and the highest recall for calm (84%). Our study demonstrates the effectiveness of RF with feature selection for speech emotion classification using a limited dataset. We found for both algorithms, anger is misclassified mostly with happy, disgust with sad and neutral, and fear with sad. This could be due to the similarity of some acoustic features between these emotions, such as pitch, intensity, and tempo.

Comparison of Convolutional Neural Network (CNN) Models for Lettuce Leaf Width and Length Prediction

Comparison of Convolutional Neural Network (CNN) Models for Lettuce Leaf Width and Length Prediction

Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.

Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, and segmentation. Machine learning algorithms such as CNNs or Transformers are subject to an inductive bias, which can have a significant impact on the performance of machine learning models. This is especially relevant for medical image segmentation applications where limited training data are available, and a model's inductive bias should help it to generalize well. In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For performance assessment, 272 FDG PET-CT scans of a clinical trial (ACRIN 6685) were utilized, which includes a total of 650 lesions (primary: 272 and secondary: 378). The image data used are highly diverse and representative for clinical use. For performance analysis, several error metrics were utilized. The achieved Dice coefficient ranged from 0.833 to 0.809 with the best performance being achieved by CNN-based approaches. U-Net-CBAM, which utilizes spatial and channel attention, showed several advantages for smaller lesions compared to the standard U-Net. Furthermore, our results provide some insight regarding the image features relevant for this specific segmentation application. In addition, results highlight the need to utilize primary as well as secondary lesions to derive clinically relevant segmentation performance estimates avoiding biases.

Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification

Chili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not addressed promptly, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and effective. Common chili plant diseases include chili leaf yellowing disease, chili leaf curling disease, cercospora leaf spots, and magnesium deficiency with symptoms that can be observed through the shape and color of the leaves. This research aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model performance using the Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001, produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential.

Comparison of Convolutional Neural Network (CNN) Models in Face Classification of Papuan and Other Ethnicities

The face of the Papuan people is unique when compared to other ethnic groups in Indonesia, even in Papua itself there are tribes whose differences can also be identified in general, namely coastal people and mountainous people. determined to identify Papuan faces. The faces of Papuans can be identified with the architectural models in Convolutional Neural Networks (CNN) such as VGG16, VGG-19, ResNet-50 and MobileNet v1 and Mobilenet v2. The best model is the Mobile Net v1 architectural model for Papuan and Non-Papuan Face Recognition with 0.95 or 95% accuracy.

Performance Comparison of Convolutional Neural Network for COVID-19 Diagnosis

Performance Comparison of Convolutional Neural Network for COVID-19 Diagnosis

Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection

Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.

Comparison of Convolutional Neural Network for Classifying Lung Diseases from Chest CT Images

This paper proposes a convolutional neural network for diagnosing various lung illnesses from chest CT images based on a customized Medical Image Analysis and Detection network (MIDNet18). With simplified model building, minimal complexity, easy technique, and high-performance accuracy, the MIDNet-18 CNN architecture classifies binary and multiclass medical images. Fourteen convolutional layers, 7 pooling layers, 4 dense layers, and 1 classification layer comprise the MIDNet-18 architecture. The medical image classification process involves training, validating, and testing the MIDNet-18 model. In the Lung CT image binary class dataset, 2214 images as training set, 1800 images as validation set, and 831 as test set are considered for classifying COVID images and normal lung images. In the multiclass dataset, 6720 images as training sets belonging to 3 classes, 3360 images as validation sets and 601 images as test sets are considered for classifying COVID, cancer images and normal images. Independent sample size calculated for binary classification is 26 samples for each group. Similarly, 10 sample sizes are calculated for multiclass dataset classification keeping GPower at 80%. To validate the performance of the MIDNet18 CNN architecture, the medical images of two different datasets are compared with existing models like LeNet-5, VGG-16, VGG-19, ResNet-50. In multiclass classification, the MIDNet-18 architecture gives better training accuracy and test accuracy, while the LeNet5 model obtained 92.6% and 95.9%, respectively. Similarly, VGG-16 is 89.3% and 77.2% respectively; VGG-19 is 85.8% and 85.4%, respectively; ResNet50 is 90.6% and 99%, respectively. For binary classification, the MIDNet18 architecture gives better training accuracy and test accuracy, while the LeNet-5 model has obtained 52.3% and 54.3%, respectively. Similarly, VGG 16 is 50.5% and 45.6%, respectively; VGG-19 is 50.6% and 45.6%, respectively; ResNet-50 is 96.1% and 98.4%, respectively. The classified images are further predicted using detectron-2 model and the results identify abnormalities (cancer, COVID-19) with 99% accuracy. The MIDNET18 is significantly more accurate than LeNet5, VGG19, VGG16 algorithms and is marginally better than the RESNET50 algorithm for the given lung binary dataset (Bonferroni — one-way Anova and pairwise comparison of MIDNET, LeNet5, VGG19, VGG16, and RESNET 50 ([Formula: see text])). The proposed MIDNet18 model is significantly more accurate than LeNet5, VGG19, VGG16, ResNet50 algorithms in classifying the diseases for the given multiclass lung dataset (Bonferroni — one-way Anova and pairwise comparison of MIDNET18, LeNet5, VGG19, VGG16, ResNet50 ([Formula: see text])).

Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images.

Deep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through high-speed video (HSV) images and computer-assisted tools, a comparison of convolutional neural network models and their accuracy will be presented. The data have been collected by an Ear Nose Throat (ENT) specialist with a 90° rigid scope in the years from 2007 to 2019, where 15.732 high-speed videos have been collected from 7909 patients. A total of 4000 images have been carefully selected, 2000 images were of normal vocal folds and 2000 images were of vocal folds with varying degrees of vocal fold nodules. These images were then split into training-, validation-, and testing-data set, for use with a CNN model with 5 layers (CNN5) and compared to other models: VGG19, MobileNetV2, and Inception-ResNetV2. To compare the neural network models, the following evaluation metrics have been calculated: accuracy, sensitivity, specificity, precision, and negative predictive values. All the trained CNN models have shown high accuracy when applied to the test set. The accuracy is 97.75%, 83.5%, 91.5%, and 89.75%, for CNN5, VGG19, MobileNetV2, and InceptionResNetV2, respectively. Precision was identified as the most relevant performance metric for a study that focuses on the classification of vocal fold nodules. The highest performing model was MobilNetV2 with a precision of 97.7%. The average accuracy across all 4 neural networks was 90.63% showing that neural networks can be used for classifying vocal fold nodules in a clinical setting.

Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.

In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy (Acc), balanced accuracy (BA), F1 and F2 from the general F macro score, Matthew’s Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman–Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.

A Comparison of Convolutional Neural Networks and Traditional Feature-Based Classification Applied to Leukaemia Image Analysis.

The accuracy of smear test image classification is a fundamental aspect in differentiating the type of leukaemia and determining the right treatment to improve the patient's chances of survival and recovery. Image Classification has lately become a very effective tool in detecting and analysing the right type of leukaemia as each type of the disease looks differently when evaluated under microscope. This paper is evaluating and comparing the efficiency and performance of feature extraction techniques (colour descriptors and Haralick texture descriptors) and a CNN (Convolutional Neural Network) built and trained by using the TensorFlow packages for classifying leukaemia images. Extracting texture and colour features from a given set of leukaemia images through computation was successful in detecting the type of disease and the results analysed with Weka Classifiers were giving the highest accuracy of 93.58%. TensorFlow tested with Cross-Validation proves efficient in training and customising the system, but the accuracy was median 56% and was not greatly improved by addressing the class imbalance issue from the data set with SMOTE. Further studies will investigate increasing the number of images by using a segmentation and image manipulation/augmentation techniques and increasing the accuracy of CNN through the addition of the investigated traditional features.