Multi-class Image Classification Research Articles

Quantum convolutional neural networks for multiclass image classification

Quantum convolutional neural networks for multiclass image classification

TyBox: An Automatic Design and Code Generation Toolbox for TinyML Incremental On-Device Learning

Incremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is typically carried out on the Cloud and inference only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device TinyML allows both the inference and the training of TinyML models directly on tiny devices. This ability paves the way for TinyML-enabled intelligent devices that can learn directly on the field and adapt to evolving environments, different working conditions, or specific users. The literature in this field is quite limited with very few solutions focusing only on the incremental fine-tuning of machine learning models, whereas a general solution encompassing algorithms and code generation for incremental on-device TinyML is still perceived as missing. The aim of this article is to introduce, to the best of our knowledge for the first time in the literature, a toolbox called TyBox for the automatic design and code generation of incremental on-device TinyML classification models. In more detail, starting from a “static” TinyML model, TyBox is able to (i) automatically design the “incremental” on-device version of the TinyML model that has been suitably designed to take into account the technological constraint on the RAM memory of the target tiny device, and (ii) autonomously provide the C++ codes and libraries to support the inference and learning of the incremental on-device TinyML model directly on the tiny devices. TyBox has been extensively compared with a state-of-the-art incremental learning solution for TinyML and tested on an off-the-shelf tiny device (i.e., the Arduino Nano 33 BLE) in three relevant TinyML application tasks and scenarios: binary image classification, multi-class image classification, and ultra-wide-band human activity recognition. In addition, TyBox is released to the scientific community as a public repository.

Hybrid quantum neural network structures for image multi-classification

Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitable for low-dimensional data and require dimensionality reduction and quantum encoding. Two image classification methods have recently emerged: one employs PCA for dimensionality reduction and angular encoding, and the other integrates QNN into a CNN to improve performance. Despite numerous algorithms, the differences between them remain unclear. This study explores these algorithms’ performance in multi-class image classification and proposes an optimized hybrid quantum neural network suitable for the current environment. As the number of classes increases, research on PCA-based quantum algorithms reveals the barren plateau problem of QNN, which is not suitable for multiple classes in a hybrid setting. Our proposed model combining traditional CNN with QNN addresses QNN’s multi-class training difficulties to some extent and achieves satisfactory classification results. Nevertheless, its accuracy remains below that of the top-performing CNN models. Furthermore, we investigate transfer learning in hybrid quantum neural network models and assess the performance of our models on the quantum hardware from IBM. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.

Hand-held GPU accelerated device for multiclass classification of X-ray images using CNN model

Chest X-ray (CXR) images are the primary investigation aid for many lung diseases and their follow-ups. For diagnosis of SARS-CoV-2, RT–PCR test and chest Computed Tomography (CT) are commonly used but both face false negatives for ruling out the infection. So, there is a demanding need for developing a system combined with Artificial Intelligence (AI) and CXR imaging to detect COVID-19 patients to avoid its spread. Here, a robust and efficient handheld device is proposed. It uses the computational power of the Graphics Processing Unit (GPU) and pre-trained deep learning models for analyzing the CXR images. A Resnet-50 CNN model is deployed on an NVIDIA Jetson Nano GPU module for the real-time classification of COVID-19, Tuberculosis, and Normal using CXR images. The device can perform real-time classification of CXR images from a portable X-ray machine and classify the image into one of the above categories. For the extensive training, a database of 680 COVID-19, 1230 Tuberculosis, and 1050 normal CXR images are extracted by combining several global databases like Kaggle, SIRM, RSNA, and Radiopaedia. The classification accuracy, precision, and loss rate were 0.9879, 0.9758, and 0.0196 respectively and our model would improve with larger data sets. The highly accurate and high-performance GPU device significantly plays a far-reaching role in COVID-19 diagnosis using Chest X-ray, which could be beneficial to triage the health system and to combat the outbreak of COVID-19.

VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis

Eye problems can lead to vision loss and have a significant impact on daily life, underscoring the critical importance of early diagnosis and treatment to prevent further damage and complications. A comprehensive segmentation-classification framework – VisionDeep-AI is developed in the proposed work for retinal vessel segmentation and multi-class classification on given fundus images. A weighted bi-directional feature pyramid network and U-Net backbone architecture-based customized deep-learning model is built to segment blood vessels which enhances feature extraction and multi-scale feature fusion. The architectural design comprises an end-to-end encoder-decoder network featuring six-depth layers with varying resolutions, enabling the extraction of high-level descriptors and lower-level, fine-grained characteristics. Furthermore, a multi-modal deep feature fusion architecture is developed for the multi-class classification of fundus images in four different categories by integrating features from segmented vessel images and raw fundus images, thereby accommodating more diversified information. A thorough analysis of VisionDeep-AI has been done on a comprehensive colour fundus image dataset. To ensure reliable results, data augmentation was done to prevent the over-fitting of the model and to enhance its generalization capability. The segmentation model performed exceptionally well and achieved a high accuracy of 97.73% and a dice coefficient of 89.90% in blood vessel segmentation from fundus images. Furthermore, multi-modal deep feature-fused classification architecture achieved a test accuracy of 81.50%, with a specificity of 93.83%. These findings showcase the robustness, generalizability and efficiency of the proposed VisionDeep-AI framework for fundus image segmentation and retinal disease classification, contributing towards advanced medical technologies and diagnosis precision.

Superposition-enhanced quantum neural network for multi-class image classification

Quantum neural networks have made progress in classification tasks. However, they face challenges when applied to multi-class image classification tasks. In this paper, we propose a superposition-enhanced quantum neural network(SEQNN). Comprising image superposition and quantum binary classifiers(QBCs), SEQNN addresses the following challenges. Firstly, the inherent linearity of quantum evolution is overcome by the one-vs-all strategy combined with QBCs, thereby circumventing the nonlinearity. Subsequently, the second challenge pertains to data imbalance within the subtasks of the one-vs-all strategy. Drawing inspiration from the mixup technique, image superposition is employed to alleviate this imbalance. Two image superposition methods, quantum state superposition(QSS) and angle superposition(AS), are proposed. The simulated experiments on MNIST and Fashion-Mnist show that AS is better than QSS in multi-class image classification tasks. Equipped with AS, SEQNN outperforms existing models and achieves an accuracy of 87.56% on MNIST.

An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images.

Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier screening of BC can significantly reduce the mortality rate and assist the diagnostic process to increase the survival rate. Researchers employ deep learning (DL) techniques to detect BC using mammogram images. However, these techniques are resource-intensive, leading to implementation complexities in real-life environments. The performance of convolutional neural network (CNN) models depends on the quality of mammogram images. Thus, this study aimed to build a model to detect BC using a DL technique. Image preprocessing techniques were used to enhance image quality. The authors developed a CNN model using the EfficientNet B7 model's weights to extract the image features. Multi-class classification of BC images was performed using the LightGBM model. The Optuna algorithm was used to fine-tune LightGBM for image classification. In addition, a quantization-aware training (QAT) strategy was followed to implement the proposed model in a resource-constrained environment. The authors generalized the proposed model using the CBIS-DDSM and CMMD datasets. Additionally, they combined these two datasets to ensure the model's generalizability to diverse images. The experimental findings revealed that the suggested BC detection model produced a promising result. The proposed BC detection model obtained an accuracy of 99.4%, 99.9%, and 97.0%, and Kappa (K) values of 96.9%, 96.9%, and 94.1% in the CBIS-DDSM, CMMD, and combined datasets. The recommended model streamlined the BC detection process in order to achieve an exceptional outcome. It can be deployed in a real-life environment to support physicians in making effective decisions. Graph convolutional networks can be used to improve the performance of the proposed model.

A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images

A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images

A variable metric proximal stochastic gradient method: An application to classification problems

Due to the continued success of machine learning and deep learning in particular, supervised classification problems are ubiquitous in numerous scientific fields. Training these models typically involves the minimization of the empirical risk over large data sets along with a possibly non-differentiable regularization. In this paper, we introduce a stochastic gradient method for the considered classification problem. To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions. Further, we utilize a non-monotone line search to automatize step size selection. Convergence results are provided for both convex and non-convex objective functions. Extensive numerical experiments verify that the suggested approach performs on par with state-of-the-art methods for training both statistical models for binary classification and artificial neural networks for multi-class image classification. The code is publicly available at https://github.com/koblererich/lisavm.

Transfer Learning for Alzheimer's Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network

Alzheimer's disease (AD) is an irreversible neurological disorder that causes the gradual decline of one's cognitive abilities, and thus, early detection is significant to slow down its deterioration. Magnetic resonance imaging (MRI) images have been commonly used to diagnose AD. Furthermore, deep learning techniques such as the Convolutional Neural Network (CNN) are utilized to assist the diagnosis due to the complexity of MRI's analysis. However, CNN models require large datasets for training and have a challenging nature for model optimization. Thus, Transfer Learning, an emerging method that can improve the performance of the deep learning model by eliminating the need for training from scratch, is introduced. This paper will propose a Transfer Learning-based EfficientNet-B0 model to classify MRI brain images for AD diagnosis. The MRI images are obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database; only axial-plane images are used in this study. As a result, the multi-class classification of MRI images into AD, MCI, and NC classes using a Transfer Learning-based model resulted in a training accuracy of 98.93% and a validation accuracy of 87.17%. These results evidenced the significance of Transfer Learning in improving model performance.

Advancing deep learning-based detection of floating litter using a novel open dataset

Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA). However, there is no study currently reporting a rigorous evaluation of the effectiveness of these approaches for floating litter detection and their effects on the models' generalization capability. To overcome the problem of limited data availability, this work introduces the “TU Delft—Green Village” dataset, a novel labeled dataset of 9,473 camera and phone images of floating macroplastic litter and other litter items, captured using experiments in a drainage canal of TU Delft. We use the new dataset to conduct a thorough evaluation of the detection performance of five DL architectures for multi-class image classification. We focus the analysis on a systematic evaluation of the benefits of TL and DA on model performances. Moreover, we evaluate the generalization capability of these models for unseen litter items and new device settings, such as increasing the cameras' height and tilting them to 45°. The results obtained show that, for the specific problem of floating litter detection, fine-tuning all layers is more effective than the common approach of fine-tuning the classifier alone. Among the tested DA techniques, we find that simple image flipping boosts model accuracy the most, while other methods have little impact on the performance. The SqueezeNet and DenseNet121 architectures perform the best, achieving an overall accuracy of 89.6 and 91.7%, respectively. We also observe that both models retain good generalization capability which drops significantly only for the most complex scenario tested, but the overall accuracy raises significantly to around 75% when adding a limited amount of images to training data, combined with flipping augmentation. The detailed analyses conducted here and the released open source dataset offer valuable insights and serve as a precious resource for future research.

Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning framework

The expertise of radiologists plays a pivotal role in the intricate task of diagnosing brain tumors. However, the escalating number of patients has rendered traditional diagnostic methods increasingly formidable and expensive. Consequently, researchers have diligently delved into the realm of medical image analysis to seek more efficient and precise approaches for the detection and classification of brain malignancies. In recent times, the surge in interest surrounding Deep Learning (DL) techniques has been notable, particularly in the development of automated systems with the capability to swiftly and reliably categorize brain tumors. The essence of this study revolves around the endeavor to confront the technical challenges inherent in this domain, with a specific focus on the intricate task of classifying brain tumors through the analysis of magnetic resonance images (MRI). The crux of the proposed model for brain tumor classification rests upon the foundation of Convolutional Neural Networks (CNNs), further fortified by a multi-branch network enriched with an inception block, elegantly incorporating a five-fold cross-validation methodology. This comprehensive approach was meticulously calibrated and evaluated using the Br35H dataset, a repository encompassing 395 images devoid of tumors, 826 images depicting gliomas, 822 portraying meningiomas, and 827 capturing pituitary tumors. This research diligently concentrates on discerning among three tumor types, including scenarios where no tumors are present, and the outcomes are meticulously juxtaposed against an array of cutting-edge classification models, such as the Support Vector Classifier (SVC), Decision Tree Classifier, Logistic Regression, and Gaussian Naive Bayes. To provide a thorough assessment of the proposed model's prowess, a battery of performance metrics was subjected to intense scrutiny, spanning accuracy, loss, and variance. The meticulous analysis culminated in the revelation that the CNN with Multi-Branch Network and Inception block exhibited unparalleled excellence, achieving an awe-inspiring accuracy rate of 99.30% and a scarcely perceptible variance of 0.0005 in its classification of the four distinct tumor classes, firmly establishing its supremacy over the existing state-of-the-art models in the field.

A color-based deep-learning approach for tissue slide lung cancer classification

Non-Small Cell Lung Cancer is the most common type of lung cancer, accounting for more than 80% of all cases. The analysis of histopathological images is the appropriate way to detect lung cancer in its initial stages. However, due to the heterogeneous patterns of molecular structures and the high magnification factor, the classification of whole slide images is difficult. Furthermore, these large-pixel images cannot be considered directly due to the high computational requirements. Several deep-learning models have been developed for the classification of histopathological images, but only in the default RGB format. Existing models are complex and rely on pathologist annotations which increases the computational cost and time. The present paper proposes a deep-learning framework, a Color-based Dilated Convolutional Neural Network (CD-CNN) for the multi-class classification of patch-based whole slide images of lung cancer. The proposed CD-CNN framework includes data preparation, color space transformation, and the classification stages. It thoroughly examines the impact of five color spaces on classification performance. The proposed model was also compared to three pre-trained models and the Convolutional Neural Network model on three different datasets namely: The Cancer Genome Atlas dataset, the Clinical Proteomic Tumor Analysis Consortium cohorts, and the LC25000 dataset. The analysis of all the models reveals that the HSV color space outperforms the other transformations for all of the datasets tested. The results depict that the proposed CD-CNN achieved the best classification results in HSV color space for all three datasets i.e. accuracy of 0.97–0.99, precision, recall, and F1 score 0.97–0.98 and AUC value of 0.970–0.984. In addition, the highest value of the kappa score 0.986 has been achieved by the proposed model. These findings demonstrate the efficacy of the proposed method and show an improvement in diagnostic systems.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

Fisher Regularized ε-Dragging for Image Classification

Discriminative least squares regression (DLSR) has been shown to achieve promising performance in multi-class image classification tasks. Its key idea is to force the regression labels of different classes to move in opposite directions by means of the ε-dragging technique, yielding discriminative regression model exhibiting wider margins. However, the ε-dragging technique ignores an important problem: its relaxation matrix is dynamically updated in optimization, which means the dragging values can also cause the labels from the same class to be uncorrelated. In order to learn a more powerful projection, as well as regression labels, we propose a Fisher regularized ε-dragging framework (Fisher-ε) for image classification by constraining the relaxed labels using the Fisher criterion. On one hand, the Fisher criterion improves the intra-class compactness of the relaxed labels during relaxation learning. On the other hand, it is expected further to enhance the inter-class separability of ε-dragging. Fisher-ε for the first time ever attempts to integrate the Fisher criterion and ε-dragging technique into a unified model because they are complementary in learning discriminative projection. Extensive experiments on various datasets demonstrate that the proposed Fisher-ε method achieves performance that is superior to other state-of-the-art classification methods. The Matlab codes are available at https://github.com/chenzhe207/Fisher-epsilon.

Patch-Based Siamese 3D Convolutional Neural Network for Early Alzheimer's Disease Using Multi-Modal Approach

Alzheimer's Disease (AD), the most common type of dementia, is characterized by memory issues that worsen with time. When evaluating single modalities, there are specific efficient learning techniques that produce a poor identification rate. The clinical significance of Mild Cognitive Impairment (MCI) has clinical importance, which is crucial for identifying and categorizing AD patients. This study includes the utilization of 3D T1 weighted /Magnetic Resonance Imaging (3D T1WI MRI) images where the structural changes in the brain are identified for predicting early AD. The classification accuracy for AD, MCI, and Normal Control (NC) has also increased due to cognitive evaluations when comparing three one vs one classifications, i.e. AD vs. NC, MCI vs. NC, AD vs. MCI, and multi-class classification. The proposed approach’s novelty is a binary and multi-class classification of structural MRI images using a Siamese 3D Convolutional Neural Network (Siamese 3D-CNN). The Stochastic Gradient Descent (SGD) has been employed to modify the proposed network's weights, and 5-Fold Cross-Validation (CV) has been used for each binary classification task. Our study used the neuropsychological data of 417 people from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the suggested methodology. Experimental results demonstrate that accuracy, sensitivity, and specificity for three binary classifications produce the maximum percentage, in contrast to other relevant approaches found in the literature. In addition, the multi-class classification accuracy of 99.8% was attained.

Ensembled EfficientNetB3 architecture for multi-class classification of tumours in MRI images

Healthcare informatics is one of the major concern domains in the processing of medical imaging for the diagnosis and treatment of brain tumours all over the world. Timely diagnosis of abnormal structures in brain tumours helps the clinical applications, medicines, doctors etc. in processing and analysing the medical imaging. The multi-class image classification of brain tumours faces challenges such as the scaling of large dataset, training of image datasets, efficiency, accuracy etc. EfficientNetB3 neural network scales the images in three dimensions resulting in improved accuracy. The novel neural network framework utilizes the optimization of an ensembled architecture of EfficientNetB3 with U-Net for MRI images which applies a semantic segmentation model for pre-trained backbone networks. The proposed neural model operates on a substantial network which will adapt the robustness by capturing the extraction of features in the U-Net encoder. The decoder will be enabling pixel-level localization at the definite precision level by an average ensemble of segmentation models. The ensembled pre-trained models will provide better training and prediction of abnormal structures in MRI images and thresholds for multi-classification of medical image visualization. The proposed model results in mean accuracy of 99.24 on the Kaggle dataset with 3064 images with a mean Dice score coefficient (DSC) of 0.9124 which is being compared with two state-of-art neural models.

Detecting COVID-19 Effectively with Transformers and CNN-Based Deep Learning Mechanisms

The COVID-19 pandemic has been a major global concern in the field of respiratory diseases, with healthcare institutions and partners investing significant resources to improve the detection and severity assessment of the virus. In an effort to further enhance the detection of COVID-19, researchers have investigated the performance of current detection methodologies and proposed new approaches that leverage deep learning techniques. In this article, the authors propose a two-step transformer model for the multi-class classification of COVID-19 images in a patient-aware manner. This model is implemented using transfer learning, which allows for the efficient use of pre-trained models to accelerate the training of the proposed model. The authors compare the performance of their proposed model to other CNN models commonly used in the detection of COVID-19. The experimental results of the study show that CNN-based deep learning networks obtained an accuracy in the range of 0.76–0.92. However, the proposed two-step transformer model implemented with transfer learning achieved a significantly higher accuracy of 0.9735 ± 0.0051. This result indicates that the proposed model is a promising approach to improving the detection of COVID-19. Overall, the findings of this study highlight the potential of deep learning techniques, particularly the use of transfer learning and transformer models, to enhance the detection of COVID-19. These approaches can help healthcare institutions and partners to reduce the time and difficulty in detecting the virus, ultimately leading to more effective and timely treatment for patients.

Multi-Class classification of prostate cancer MRI images based on UCLA score using RegNetY-320 model

Prostate cancer (PCa) is the most frequent non-skin cancer in males, and it represents a significant global healthcare problem. Another challenge is the development of an accurate classification model for the detection of PCa. As a result, developing an accurate classification model for PCa is extremely important and has potentially significant clinical implications. These models have the potential to boost the benefits of treatment while also increasing the likelihood of a patient's survival. The PCa is classified in this research based on the T2w magnetic resonance images (MRI), which are collected from the prostate dataset. In this work, a deep learning-based approach RegNetY-320 model is proposed to classify and detect the PCa. The RegNet model is a pre-trained deep transfer learning architecture, which is most commonly used for the image classification process. The proposed model is optimized using various optimizers, including Adam, AdaMax, SGD, RMSprop, Ftrl, and Nadam, and the optimized models are termed model-01 to model-06. For the evaluation, a multiclass classification system was employed to classify 845 patient records from a mpMRI dataset with a unique "UCLA" score of the ROI. Accuracy, precision, sensitivity, specificity, and the F1 score are all calculated based on the classification for the purpose of performance analysis. Finally, in accordance with the findings, the performances of the various proposed models are evaluated in order to determine their validity. When compared among proposed models, the proposed model RegNetY-320_Model-03 optimized using the RMSprop optimizer has obtained greater performance with 95.70 percent accuracy, which is the highest accuracy performance obtained in this research.

Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

The purpose of this study was to evaluate the performance of convolutional neural network-based models for the detection and classification of maxillofacial fractures in computed tomography (CT) maxillofacial bone window images. A total of 3407 CT images, 2407 of which contained maxillofacial fractures, were retrospectively obtained from the regional trauma center from 2016 to 2020. Multiclass image classification models were created by using DenseNet-169 and ResNet-152. Multiclass object detection models were created by using faster R-CNN and YOLOv5. DenseNet-169 and ResNet-152 were trained to classify maxillofacial fractures into frontal, midface, mandibular and no fracture classes. Faster R-CNN and YOLOv5 were trained to automate the placement of bounding boxes to specifically detect fracture lines in each fracture class. The performance of each model was evaluated on an independent test dataset. The overall accuracy of the best multiclass classification model, DenseNet-169, was 0.70. The mean average precision of the best multiclass detection model, faster R-CNN, was 0.78. In conclusion, DenseNet-169 and faster R-CNN have potential for the detection and classification of maxillofacial fractures in CT images.