Optimal Convolutional Neural Network Research Articles

Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks

PurposeTo determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edited magnetic resonance spectroscopy (MRS) data. MethodsAn ablation study using simulated data was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared using both simulated and in vivo data to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using the signal-to-noise ratio (SNR) and linewidth of the GABA peak, choline artifact, and by visually assessing the reconstructed final difference spectrum. Statistical significance and effect size were assessed using the Wilcoxon signed rank test. ResultsThe ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with complex convolutions was most effective for FPC. Overall, in the comparative study using simulated data, our CC-CNN model (that received complex-valued inputs with complex convolutions) outperformed the other models as evaluated by the mean absolute error. ConclusionOur results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and complex convolutions. Overall, this model outperformed existing DL models.

Development of a facial recognition model using optimized convolution neural network with Tiny-ML and fingerprint authentication for safe lock entry

Safe boxes have been used for centuries to protect valuable items, and their design and construction have evolved over time to improve security. In this study, we carried out the design and implementation of a facial recognition technology using optimized convolutional neural networks (CNNs) with TinyML and fingerprint. The facial recognition system is designed using pre-trained CNN architectures then optimized with TinyML to make it fit into resource constrained edge device, the fingerprint recognition is designed using ridge matching biometric algorithm which fits in perfectly into the microcontroller-based system. The safe box was fabricated using sheet metal and insulated using heat resistive material (fiber glass), components like OV2640 camera module, ESP32-S3, R305 Fingerprint sensor, 4.3 inches touch screen TFT display and a charging module to manage and charge the systems battery were used. The system was implemented to be a level two security and two-way verification system using secured Wi-Fi to access the system before the facial and fingerprint recognition functionality is activated, the sequence of operation can either be facial recognition before fingerprint recognition or fingerprint recognition before facial recognition. Evaluating the system’s facial recognition performance with confusion matrix, of all the optimized CNN architectures like VGGNet, SENet, RestNet, MobileNet-V2, EfficientNet-B0, SENet Architecture performed best outputting an accuracy of 98%, precision of 92%, also SENet unoptimized model performed same, MobileNet-V2 performed optimally in the inference speed test with a speed of 15ms/images with the fingerprint also evaluated using confusion matrix and perform with 98% accuracy. It was noticed that the inference speed of the unoptimized model is faster than that of the TinyML optimized model which is justifiable based on the processing clock speed, memory usage, GPU and architecture of the microcontroller/microprocessor. From the result obtained TinyML proves to be suitable mean for edge AI (artificial intelligence) and is recommended for future research and development.

Statement of Retraction: Optimal convolutional neural network classifier for asthma disease detection using speech signals

Statement of Retraction: Optimal convolutional neural network classifier for asthma disease detection using speech signals

RETRACTED ARTICLE: Optimal convolutional neural network classifier for asthma disease detection using speech signals

ABSTRACT Statement of Retraction: Optimal convolutional neural network classifier for asthma disease detection using speech signals We, the Editors and Publisher of the International Journal of Healthcare Management, have retracted the following article: Md. Asim Iqbal, K. Devarajan & Syed Musthak Ahmed (06 Feb 2023): Optimal convolutional neural network classifier for asthma disease detection using speech signals, International Journal of Healthcare Management, DOI: 10.1080/20479700.2023.2173774 Since publication, significant concerns have been raised about the integrity of the data and reported results in the article. When approached for an explanation, the authors did not provide their original data or any necessary supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Infrared microspectroscopy and machine learning: A novel approach to determine the origin and variety of individual rice grains

Accurately distinguishing the origin and variety of rice types is of paramount importance to conducting research on this staple crop. While various methods are currently employed for this purpose, few approaches can verify the identity of single grains rapidly and accurately. In this study, we present a method that integrates machine learning with infrared (IR) microspectroscopy for swift detection of the origin and variety of a single rice grain. To establish the validity of our approach, we assembled a diverse collection of rice samples, comprising 14 distinct types with different origins or varieties. Each rice sample yielded 100 microspectroscopy spectra, resulting in 1400 spectra. We applied two deep learning algorithms, deep neural network (DNN) and convolutional neural network (CNN), for spectral analysis. The 1400 spectra were randomly partitioned into calibration and validation sets at a ratio of 3:1. These datasets were subjected to both DNN and CNN analysis for classification of samples by origin and variety. Following 10,000 iterations, we selected optimal DNN and CNN models. The predication accuracies of the optimal DNN model for calibration and validation sets were 95.4% and 90.0%, respectively. In comparison, the optimal CNN model demonstrated superior accuracy, with 99.8% for the calibration set and 92.0% for the validation set. Based on these results, we selected the CNN model as the final model for field use in rice grain classification.

Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization

The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result, significant attention is currently being devoted to developing methods aimed at tailoring hyperparameters for specific CNN models and classification tasks. While existing optimization methods often yield favorable image classification results, they do not provide guidance on which hyperparameters are worth optimizing, the appropriate value ranges for those hyperparameters, or whether it is reasonable to use a subset of training data for the optimization process. This work is focused on the optimization of hyperparameters during transfer learning, with the goal of investigating how different optimization methods and hyperparameter selections impact the performance of fine-tuned models. In our experiments, we assessed the importance of various hyperparameters and identified the ranges within which optimal CNN training can be achieved. Additionally, we compared four hyperparameter optimization methods—grid search, random search, Bayesian optimization, and the Asynchronous Successive Halving Algorithm (ASHA). We also explored the feasibility of fine-tuning hyperparameters using a subset of the training data. By optimizing the hyperparameters, we observed an improvement in CNN classification accuracy of up to 6%. Furthermore, we found that achieving a balance in class distribution within the subset of data used for parameter optimization is crucial in establishing the optimal set of hyperparameters for CNN training. The results we obtained demonstrate that hyperparameter optimization is highly dependent on the specific task and dataset at hand.

Automated machine learning-based neural network for brain tumor classification

In the contemporary medical landscape, characterized by the widespread and pernicious affliction of brain tumors, the imperative to enhance diagnostic modalities is paramount, aligning with the overarching objective of precisely delineating the affected patient cohort, thereby affording the prospect of administering expeditious therapeutic interventions. Within this scholarly discourse, the principal objective pertains to the discernment of an optimal Convolutional Neural Network (CNN) model, engendered through the mechanizations of automated machine learning, as instantiated within the virtual precincts of the "Edge Impulse" online platform. The corpus of investigation entails the acquisition of requisite data sets from the digital repository denominated "Kaggle," specialized in the domain of scientific knowledge. The amassed data sets, having undergone meticulous preprocessing procedures, were subsequently subjected to partitioning activities within the confines of the "Edge Impulse" framework, wherein a standardized ratio of division, namely a four-to-one proportionality between training and testing subsets, was consistently maintained across discrete data clusters. The training and testing processes were accomplished on Edge Impulse. The image mode, data learning block, learning rate, et cetera, were modified for each neural network models trained on Edge Impulse. Each model performed differently, and the distinct testing accuracy and on device performances were collected for each model for comparison. The experimental results demonstrate that using transfer learning supported by Edge Impulse with learning rate equals to 0.00051 and fit longest axis image resize mode is the optimal option for training a brain classifying model on Edge Impulse through automated machine learning.

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning

The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.

Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Fault diagnosis

Various advanced reactor designs proposed in recent years envision deployment scenarios which feature reactor operations with significantly reduced staffing or even completely autonomous frameworks to reduce the operations and management costs of the plants. Many SMR and microreactor designs feature extended fuel cycles which limit inspection intervals, reduced access to critical components, and load-following capabilities that expose the reactor unit to different transients. Safe and reliable semi- or fully autonomous operations under these challenging operational regimes must be enabled through an on-line monitoring (OLM) system that effectively detects and diagnoses malfunctions in the reactor plant. This work presents the development of the Fault Diagnosis Module of the previously proposed data-driven OLM system for reactor operations: the Fault Detection and Diagnosis Monitoring System (FDDMS). The Fault Diagnosis Module monitors various sensor signatures from multiple systems and components at a nuclear power plant and accurately diagnoses the type and location of a malfunction. When integrated into the complete FDDMS methodology, the Fault Diagnosis Module can provide power transient dependent fault characterization by using separate convolutional neural network (CNN) models for steady state, ramping up in power, and ramping down in power operations, making the FDDMS especially applicable to load-following operational strategies. Two separate diagnosis approaches were explored in this paper for the Fault Diagnosis Module architecture: Hierarchical and End-to-End. The Hierarchical approach is a two-stage architecture in which the first stage uses a single CNN to classify the plant subsystem where the malfunction initiated. Subsequently in the second stage, a separate CNN is used for each subsystem to describe the specific fault type. The End-to-End approach uses a single CNN to directly classify the fault type from the overall list. To efficiently utilize computational resources, the hyperband intelligent hyperparameter optimization method to generate optimal CNN architectures. The diagnosis approaches were compared in terms of precision rate, recall rate, F1-score, and total accuracy for the possible fault types. Both diagnosis approaches produced good and satisfactory performance by generating total diagnosis accuracies above 99% on 17 different malfunction scenarios for all three power transient datasets. Additionally, robustness against noisy sensor data was tested, with models maintaining 99–100% accuracy at various levels of noise, and an illustrative real-time application of the methodology is provided.

Convolutional neural networks applied to dissolved gas analysis for power transformers condition monitoring

In this contribution a methodology to diagnose transformer faults based on Dissolved Gas Analysis (DGA) by using a convolutional neural network (CNN) is proposed. The algorithm to transform the gas contents (resulting from the DGA analysis) into feature maps is introduced, and the resulting feature maps are the input of the CNN. In order to take into account the fact that the data set is imbalanced, the improved Synthetic Minority Over-Sampling Technique (SMOTE) is combined with the data cleaning technique to protect the CNN from training bias. The effect of the CNN architecture on the classification performance is also investigated to determine the optimal CNN parameters. All the above mentioned possibilities are tested and their performance investigated; in addition, a final test on the IEC TC 10 transformer fault database validates the accuracy and the generalization potential of the proposed methodology.

Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. Recently, convolutional neural networks (CNNs) have been carried out for medical diagnosis to obtain higher accuracy than standard machine learning algorithms and specialists' expertise. Therefore, the proposed approach involves using the Bayesian hyperparameter optimization with the CNN architecture for automatic diagnosis of ear imagery database including four classes: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). The suggested approach was trained using 616 otoscopic images, and the performance of this approach was assessed using 264 testing images. In this paper, the performance of ear disease classification was compared in terms of accuracy, sensitivity, specificity, and positive predictive value (PPV). The results produced a classification accuracy of 98.10%, a sensitivity of 98.11%, a specificity of 99.36%, and a PPV of 98.10%. Finally, the suggested approach demonstrates how to locate optimal CNN hyperparameters for accurate diagnosis of ear diseases while taking time into account. As a result, the usefulness and dependability of the suggested approach will lead to the establishment of an automated tool for better categorization and prediction of different ear diseases.

ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.

Optimization of CNN using modified Honey Badger Algorithm for Sleep Apnea detection

Sleep Apnea (SA) is the most prevalent breathing sleep problem, and if left untreated, it can lead to catastrophic neurological and cardiovascular illnesses. Conventionally, polysomnography (PSG) is used to diagnose SA. Nonetheless, this approach necessitates several electrodes, cables, and a professional to oversee the experiment. A promising alternative is using a single-channel signal for SA diagnosis, with the electrocardiogram (ECG) signal being among the most relevant and easily recordable. Recently, a convolutional neural network (CNN) has been used to extract efficient features from training data instead of manually selecting characteristics from ECG. However, selecting the best hyperparameter values for CNN can be challenging due to the vast number of possibilities. To address this, we propose a modified Honey Badger Algorithm (MHBA) combined with three improvement initiatives: quasi-opposition learning, arbitrary weighting agent, and adaptive mutation method. Our approach is evaluated on the Physionet Apnea ECG database, consisting of 70 single-lead ECG recordings annotated by qualified medical professionals. The experiments show that the MHBA outperforms traditional CNN and machine learning methods with an accuracy of 91.3%, AUC of 97.5%, specificity of 93.6%, and sensitivity of 90.1%. Our results demonstrate the effectiveness of the MHBA for SA detection.

Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism

Pulmonary Embolism (PE) represents a thrombus (“blood clot”), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients death. This disorder is commonly diagnosed using Computed Tomography Pulmonary Angiography (CTPA). Deep learning holds great promise for the Computer-aided Diagnosis (CAD) of PE. However, numerous deep learning methods, such as Convolutional Neural Networks (CNN) and Transformer-based models, exist for a given task, causing great confusion regarding the development of CAD systems for PE. To address this confusion, we present a comprehensive analysis of competing deep learning methods applicable to PE diagnosis based on four datasets. First, we use the RSNA PE dataset, which includes (weak) slice-level and exam-level labels, for PE classification and diagnosis, respectively. At the slice level, we compare CNNs with the Vision Transformer (ViT) and the Swin Transformer. We also investigate the impact of self-supervised versus (fully) supervised ImageNet pre-training, and transfer learning over training models from scratch. Additionally, at the exam level, we compare sequence model learning with our proposed transformer-based architecture, Embedding-based ViT (E-ViT). For the second and third datasets, we utilize the CAD-PE Challenge Dataset and Ferdowsi University of Mashad’s PE Dataset, where we convert (strong) clot-level masks into slice-level annotations to evaluate the optimal CNN model for slice-level PE classification. Finally, we use our in-house PE-CAD dataset, which contains (strong) clot-level masks. Here, we investigate the impact of our vessel-oriented image representations and self-supervised pre-training on PE false positive reduction at the clot level across image dimensions (2D, 2.5D, and 3D). Our experiments show that (1) transfer learning boosts performance despite differences between photographic images and CTPA scans; (2) self-supervised pre-training can surpass (fully) supervised pre-training; (3) transformer-based models demonstrate comparable performance but slower convergence compared with CNNs for slice-level PE classification; (4) model trained on the RSNA PE dataset demonstrates promising performance when tested on unseen datasets for slice-level PE classification; (5) our E-ViT framework excels in handling variable numbers of slices and outperforms sequence model learning for exam-level diagnosis; and (6) vessel-oriented image representation and self-supervised pre-training both enhance performance for PE false positive reduction across image dimensions. Our optimal approach surpasses state-of-the-art results on the RSNA PE dataset, enhancing AUC by 0.62% (slice-level) and 2.22% (exam-level). On our in-house PE-CAD dataset, 3D vessel-oriented images improve performance from 80.07% to 91.35%, a remarkable 11% gain. Codes are available at GitHub.com/JLiangLab/CAD_PE.

Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph

Background/PurposeThe preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs). Materials and methods336 periapical radiographic images (teeth: 390) between January 2017 and December 2019 were collected and de-identified. All periapical radiographic image datasets were divided into training dataset (n = 82, teeth: 123) and test dataset (n = 336, teeth: 390). For creating an optimal deep CNN algorithm model, the training datasets were directly used for the segmentation and individual tooth detection. To evaluate the diagnostic power, we calculated the degree of alveolar bone loss deviation between our proposed method and ground truth, the Pearson correlation coefficients (PCC), and the diagnostic accuracy of the proposed method in the test datasets. ResultsThe periodontal bone loss degree deviation between our proposed method and the ground truth drawn by the three periodontists was 6.5 %. In addition, the overall PCC value of our proposed system and the periodontists’ diagnoses was 0.828 (P < 0.01). The total diagnostic accuracy of our proposed method was 72.8 %. The diagnostic accuracy was highest for stage III (97.0 %). ConclusionThis tool helps with diagnosis and prevents omission, and this may be especially helpful for inexperienced younger doctors and doctors in underdeveloped countries. It could also dramatically reduce the workload of clinicians and timely access to periodontist care for people requiring advanced periodontal treatment.

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

Optimized Deep Learning Model Using Modified Whale’s Optimization Algorithm for EEG Signal Classification

Brain-Computer Interface (BCI) is a technology in which Electroencephalogram (EEG) signals are utilized to create a link between a person’s mental state and a computer-based signal processing system that decodes the signals without needing muscle movement. The mental process of picturing the movement of a body component without actually moving that body part is known as Motor Imagery (MI). MI BCI is a Motor Imagery-based Brain-Computer Interface that allows patients with motor impairments to interact with their environment by operating robotic prostheses, wheelchairs, and other equipment. Feature extraction and classification are essential parts of the EEG signal processing for MI BCI. In this work, Whales Optimization Algorithm with an Improved Mutualism Phase is proposed to find the optimal Convolutional Neural Network architecture for the classification of motor imagery tasks with high accuracy and less computational complexity. The Neurosky and BCI IV 2a datasets were used to evaluate the proposed methodology. Experiments demonstrate that the suggested technique outperforms other competing methods regarding classification accuracy values at 94.1% and 87.7% for the Neurosky and BCI datasets, respectively.

Parameter training method for convolutional neural networks based on improved Hausdorff-like derivative

In this paper, we propose a parameter training method in convolutional neural networks (CNNs). To introduce the orders and the cost function into the parameter optimization in CNNs, we give a definition of the Hausdorff-like derivative by analyzing the definition of the Hausdorff derivative, and a corresponding improved form is proposed. The improved Hausdorff-like (IHL) derivative is utilized in the parameter training of the back propagation in CNNs, and the orders are introduced in the back propagation for training of parameters. Therefore, we adjust the orders by judging the size of the cost function during the parameter training in CNNs, and the tuning of the orders can flexibly adjust the training speed of the parameters. An adaptive tuning approach for the orders is presented by analyzing the rule of the orders for the IHL derivative. By combining the proposed parameter training method with the adaptive moment estimation (Adam) algorithm, we propose the Adam algorithm with the IHL derivative. The experiments based on residual networks are carried out, and the experiments show that the proposed algorithm outperforms the original algorithm.

Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network

The science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods have good accuracy. We present five Convolutional Neural Network (CNN) architectures for identifying brain tumors (glioma, meningioma, no tumor, and pituitary) on MRI images. This study aims to develop an optimal CNN architecture for identifying tumors. We use the dataset from Kaggle with a total training data of 5712 and testing of 1311. Of the five proposed CNN architectures, architecture c has the highest accuracy of 82.2% with an unlimited number of parameters of 29605060. A good CNN architecture has many convolution layers. We also compare the proposed architecture with CNN transfer learning (Inception, ResNet-50, and VGG16), and with CNN transfer learning architecture, the accuracy is higher than our proposed architecture.