CNN Classifier Research Articles

SSO-CCNN: A Correlation-Based Optimized Deep CNN for Brain Tumor Classification Using Sampled PGGAN

Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images

ABSTRACT Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system’s efficiency of detecting cancer and help the patient to get the appropriate treatment.

Optimization of block-scaled integer GeMMs for efficient DNN deployment on scalable in-order vector processors

A continuing rise in DNN usage in distributed and embedded use cases has demanded more efficient hardware execution in the field. Low-precision GeMMs with optimized data formats have played a key role in more memory and computationally-efficient networks. Recently trending formats are block-scaled representations stemming from tight HW-SW co-optimization, that compress network size by sharing exponents per data block. Prior work mostly focuses on deploying such block-scaled GeMM operations on domain-specific accelerators for optimum efficiency at the cost of flexibility and ease of deployment. In this work, we exploit and optimize the deployment of block-scaled GeMMs on fully-programmable in-order vector processors using ARM SVE. We define a systematic methodology for performing design space exploration to optimally match the workload specifications with processor vector-lengths, different microkernels, block sizes and shapes. We introduce efficient intrinsics-based microkernels with effective loop unrollings, and data-transfer efficient fused requantization strategies to maximize kernel performance, while also ensuring several deployment configurations. We enable generalized block-scaled kernel deployments through tunable block sizes and shapes, which helps in accommodating different accuracy-speed trade-off requirements. Utilizing 2D activation blocks instead of conventional 1D blocks, the static and dynamic BS-INT8 configurations yielded on average 3.8x and 2.9x faster speedups over FP32 models respectively, at no accuracy loss for CNN classification tasks on CIFAR10/100 datasets.

MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN.

Circular RNAs (circRNAs) exist in vivo and are a class of noncoding RNA molecules. They have a single-stranded, closed, annular structure. Many studies have shown that circRNAs and diseases are linked. Therefore, it is critical to build a reliable and accurate predictor to find the circRNA-disease association. In this paper, we presented a meta-learning model named MAMLCDA to identify the circRNA-disease association, which is based on model-agnostic meta-learning (MAML) combined with CNN classification. Specifically, similarities between diseases and circRNAs are extracted and integrated to characterize their relationships, and k-means is used to cluster majority samples and select a certain number of samples from each cluster to obtain the same number of negative samples as the positive samples. To further reduce the dimension of the features and save operation time, we applied probabilistic principal component analysis (PPCA) to compact the integrated circRNA and disease similarity network feature vectors. The feature vectors are converted into images. At this time, the prediction problem is transformed into the 2-way 1-shot problem of the image and input into the model with MAML as the meta-learner and CNN as the base-learner. Comparison results of five-fold cross-validation on two benchmark datasets illustrate that MAMLCDA outperforms several state-of-the-art approaches with the best accuracies of 95.33% and 98%. Therefore, MAMLCDA can help to understand the pathogenesis of complex diseases at the circRNA level.

Multi-branch CNN and grouping cascade attention for medical image classification

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

Flash Flood Detection and Alert System Using Machine Learning

Abstract: Floods can be considered as one among the top natural disasters that sometimes happens regularly in particular season thereby causing harm on human lives and also resultsin reducing economic growth. So, the news about the flooding events has to be spread to nearby localities in time so as to avoid further chaos. Therefore, it is crucial to build a warning system that informs the flooding event to reduce the casualties of flood disaster. Recognizing sensitive events in images, such as flood events is significant for the maintenance of normal public opinionand social stability. By now, it is still a challenging problem. In this project, we propose a novel method for recognizing flood events from images using Keras pretrained MobileNet CNN. Flooding and non-flooding images are collected and trained in theCNN network so as to build the classifier that can differentiate the input image from flooding and non-flooding categories. At first, the feature extraction is carried to develop a model that is capable of classifying flood images from normal images. Later the MobileNet CNN helps in classifying the flood images effectively with accuracy and an appropriate warning message is sent to the people in nearby localities. To enhance the efficacy of our flood event recognition system, we are integrating cutting-edge tech- nologies and methodologies. In addition to leveraging the power of Keras pretrained MobileNet CNN for image classification, we are also implementing advanced image processing techniques for better feature extraction and analysis. By incorporating thesemethods, we aim to achieve higher accuracy and reliability in identifying flood events from images.

Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection

A brain tumor is the most common disease on earth and it is harmful to people. Tumors are the uncontrolled growth of cells and tissues in the human brain called a tumor. The image is acquired using CT scans and Magnetic Resonance Images. The identification of tumors at an early stage is critical and challenging for researchers. A patient comes to the hospital when he starts suffering from pain, headache, omission etc and at that time, if he has a tumor, To recognize the tumor early stage it is very different to identify whether it is benign (non-cancerous) or malignant (cancerous), many techniques or methods are available for detection of tumor here we apply SVM algorithm and CNN on brain Magnetic Resonance Images for classification of a benign or malignant tumor. Here, we propose a system based on the new concept of simple tumor detection that uses feature extraction techniques, segmentation algorithm and classification. To identify similar patients who have or do not have a brain tumor, as well as to ascertain the type of tumor they have and their tumor sizes. By comparing both SVM & CNN which technique is more beneficial and which one is better in both? The performance of SVM classifiers is measured in terms of training effectiveness and classification accuracy. With 95% accuracy, it manages the process of brain tumor categorization in MRI scans. The efficacy of training and classification accuracy of the CNN classifier is compared (96.33%). Both methods get high accuracy but as compared to SVM, CNN provides more accuracy and consumes less time for execution.

Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images

Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images

Binarydnet53: a lightweight binarized CNN for monkeypox virus image classification

Binarydnet53: a lightweight binarized CNN for monkeypox virus image classification

Enhanced Emotion Detection Model Using Dusky Canidae Optimization-Based Deep CNN Classifier

Emotion detection involves distinguishing the complex nature of emotions, cross-cultural variations, and ethical considerations. The proposed model addresses cross-cultural issues through diverse training and ensures ethical data usage through advanced machine-learning techniques to enhance accuracy. The research aims to develop a Dusky Canidae Optimization-based Deep Convolutional Neural Network model (DCO opt deep CNN) for emotion recognition in audio-visual data, benefiting various advancements. During preprocessing, audio and video are separated to enhance sound quality and eliminate data redundancy while retaining vital information. Features are then extracted and employed for training and testing using the automation and effectiveness of Deep CNNs. The concatenated features are optimized using a hybrid approach that combines Dusky Hawk and Canidae Wolf Optimization (CWO), offering simplicity and interpretability with optimal parameter settings. The proposed DCO model effectively classifies emotions within the data. Evaluating the database, the DCO model exhibits a performance enhancement of 1.28%, 2.91%, and 1.28% in accuracy, sensitivity, and specificity with respect to the existing learner memorizing optimization-based deep NN model.

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images.

Polycystic Ovary Syndrome (PCOS) is a medical condition that causes hormonal disorders in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS mainly suffer from extreme weight gain, facial hair growth, acne, hair loss, skin darkening, and irregular periods, leading to infertility in rare cases. Doctors usually examine ultrasound images and conclude the affected ovary but are incapable of deciding whether it is a normal cyst, PCOS, or cancer cyst manually. To have access to the high-risk crucial PCOS and to detect the condition and the treatment aimed at mitigating health hazards such as endometrial hyperplasia/cancer, infertility, pregnancy complications, and the long-term burden of chronic diseases such as cardiometabolic disorders linked with PCOS. The proposed Self-Defined Convolution Neural Network method (SD_CNN) is used to extract the features and machine learning models such as SVM, Random Forest, and Logistic Regression are used to classify PCOS images. The parameter tuning is done with lesser parameters in order to overcome over-fitting issues. The self-defined model predicts the occurrence of the cyst based on the analyzed features and classifies the class labels effectively. The Random Forest Classifier was found to be the most reliable and accurate among Support Vector Machine (SVM) and Logistic Regression (LR), with accuracy being 96.43%. The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction.

LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images

LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

RESNET34 with Synchrosqueezing Transform for ADHD Disorder Detection Using EEG Signals

It is vital for the development of attention deficit hyperactivity disorders (ADHDs) diagnostic tools that help healthcare practitioners to have automated recognition of ADHDs. Recently, the detection of ADHD using EEG data has attracted a lot of interest due to its apparent rapidity in diagnosis and treatment. This is because the disease seems to be caused by hyperactivity and inattention. This paper presents the method for diagnosing ADHDs based on EEG data, with the nonlinear characteristics being retrieved via the Synchrosqueezing Transform (ST). The ST filter banks are used to break down the 16 channels of EEG signal information into the ideal number of time-frequency subbands. At each stage of the decomposition process, the distinctive feature vectors are assessed using CNN as well as the proposed nonlinear fractal dimensions approach. Using ResNet34, it was discovered that a modified CNN classifier that used a 10-fold cross-validation approach was an efficient classifier for differentiating between normal people and those with ADHD. The suggested method was able to successfully categorize ADHD and normal participants with the best accuracy, as shown by several performance indicators. The statistical study demonstrated that the CNN and the proposed nonlinear feature estimation techniques produce possible characteristics that are acceptable for the automated identification of ADHD. These features may be categorized with good accuracy, sensitivity and specificity. The technique that has been presented is capable of correctly differentiating between participants who have ADHD and those who do not have ADHD with an ultimate precision of 99.5%.

BXCNN: A novel depression detection model

In response to the mounting societal pressures, an increasing number of individuals grappling with mental health challenges are turning to social media platforms to express their feelings. The utilization of deep learning models for analyzing social media data has become increasingly crucial in detecting early signs of depression. Early intervention through depression detection can significantly enhance patients quality of life and even save lives. However, many existing deep learning models suffer from low prediction accuracy, exacerbated by the imbalance between positive and negative samples in the collected data. To address these challenges, we propose a novel depression detection model integrated with BERT, XGBoost, and Convolutional Neural Networks (BXCNN). This model harnesses the advantages of ensemble learning and deep learning technologies by integrating XGBoost for feature extraction to alleviate data imbalance and CNN for classification. We transform depression-related textual data into sentence vectors using BERT to capture semantic information effectively. These features are then fed into a CNN classifier to accurately predict the likelihood of individuals exhibiting depressive symptoms. Through empirical evaluations on relevant datasets, our approach excels across various evaluation metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC).

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

Dermatological Decision Support Systems using CNN for Binary Classification

Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.

Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification

Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification

Classifying Aerial Objects Based on Risk: A Machine Learning Approach

The spy balloon that flew across the U.S. in February 2023 posed a serious security threat. US security officials have said this balloon tried to gather intelligence by monitoring sensitive military sites, and as a result, the U.S. government began more closely scrutinizing its airspace to better categorize aerial objects and detect threats. However, the airspace is filled with a myriad of aerial objects, making the problem of classifying and risk determination very challenging. We hypothesize that if we label aerial objects based on the risks they pose, then a Machine Learning algorithm can be made to learn and predict the risks of previously unknown or unseen aerial objects. Currently, there are no known single datasets that contain both old and newer aerial objects, such as drones, planes, etc., nor do datasets have labels to identify the risks associated with the objects. The goal of this research is twofold: 1) We create a new comprehensive dataset that contains traditional and newer aerial objects. We label the objects as high, medium, low, or no risk based on the threat markers on the aerial objects. 2) We use the MobileNetV2 CNN classification algorithm to validate the dataset and provide accurate results. Advancements in this space can potentially help intelligence agencies and security analysts quickly assess developing scenarios and provide a reliable risk assessment for observed aerial objects.

Dual-stream spectral-spatial convolutional neural network for hyperspectral image classification and optimal band selection

Along with the high spectral rich information it provides, one of the difficulties in processing a hyperspectral image is the need for expert knowledge and high-spec hardware to process very high-dimensional data. 3D convolutional neural network(3D CNN), which uses spectral and spatial features together, enables a powerful solution for HSI classification. This study proposes an efficient dual-stream 3D CNN for accurate HSI classification.The proposed method offers effective classification using spectral-spatial features without relying on pre-processing or post-processing. A comparative study of how CNN classification performance is affected by hyperspectral band selection based on deep reinforcement learning (DRL) is presented. Using the most relevant bands in the hyperspectral image is decisive in deep CNN networks without losing information and accuracy. The proposed method was compared with 3D CNN, 3D + 1D CNN, Multiscale 3D deep convolutional neural network (M3D-DCNN), and InceptionV3 algorithms using Indian Pines (IP), Salinas, Pavia Center (PaviaC), Houston 2013 and QUH-Tangdaowan datasets. It achieved 92.43 % overall accuracy (OA) in IP, 95.06 % OA in Salinas dataset, 99.00 % OA in PaviaC dataset, 91.25 % OA in Houston 2013 and 94.87 % OA in QUH-Tangdaowan. Codes are released at: https://github.com/lapistlazuli/DS-3DCNN.