Multi-class Image Classification Research Articles

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC causes maximum deaths in women. In this work, three deep learning (DL) models such as Vision Transformer (ViT), Convmixer, and Visual Geometry Group-19 (VGG-19) are implemented for the detection and classification of different breast cancer tumors with the help of Breast cancer histopathological (Break His) image database. The performance of each model is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1-score, and area under the curve (AUC). From the simulation result, ViT showed the best performance for binary classification of breast cancer tumors with accuracy, precision, recall, and F1-score of 99.89 %, 98.29 %, 98.29 %, and 98.29 %, respectively. Also, ViT showed the best performance in terms of accuracy (98.21 %), average Precision (89.84 %), recall (89.97 %), and F1-score (88.75) for eight class classifications. Moreover, we have also ensemble the ViT-Convmixer model and observed that the performance of the ensemble model is reduced as compared to the ViT model. We have also compared the performance of the proposed best model with other existing models reported by several research groups. The study will help find suitable models that will increase accuracy in early diagnoses of BC. We hope the study will also help to minimize human errors in the early diagnosis of this fatal disease and administer appropriate treatment. The proposed model may also be implemented for the detection of other diseases with improved accuracy.

Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models

The core problem of this research is the rapid and accurate classification of natural disasters, which is essential for effective response and mitigation strategies. Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to the multi-class classification of natural disasters using image data from a Kaggle dataset encompassing Cyclone, Wildfire, Flood, and Earthquake incidents. The method used is an ensemble learning model that combines the strengths of the InceptionV3 model and a custom Convolutional Neural Network (CNN). The result of this study is an ensemble model that achieves a commendable accuracy of 92.79%, surpassing individual models and demonstrating the efficacy of combining diverse features extracted by InceptionV3 and CNN architectures. Additionally, a standalone CNN model is implemented, achieving an accuracy of 88.76%. The research concludes that the ensemble model’s superior performance makes it a valuable tool for the multi-class classification of natural disaster images.

Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs. For example, in Class 0 vs. All, EBNN achieved a UAcc of 92.6%, UAUC-ROC of 95.0%, and a Brier Score of 0.157, significantly surpassing BNN's performance. Similarly, EMC Dropout excelled in Class 1 vs. All with a UAcc of 83.5%, UAUC-ROC of 95.8%, and a Brier Score of 0.165. These advanced models demonstrated higher accuracy, better discriaminative capability, and more accurate probabilistic predictions. Our findings highlight the efficacy of DNN with UQ in enhancing model reliability and interpretability, making them highly suitable for critical healthcare applications like chest X-ray imageQ6 classification.

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.

Knee Osteoporosis Diagnosis Based on Deep Learning

Osteoporosis, a silent yet debilitating disease, presents a significant challenge due to its asymptomatic nature until fractures occur. Rapid bone loss outpaces regeneration, leading to pain, disability, and loss of independence. Early detection is pivotal for effective management and fracture risk reduction, yet current diagnostic methods are time-consuming. Despite its importance, research addressing early diagnosis remains limited. Deep learning, particularly convolutional neural networks (CNNs), has emerged as a potent tool in image analysis. This paper presents a novel approach utilizing transfer learning with CNNs for osteoporosis detection from X-ray images. The proposed approach not only achieves a high accuracy of osteoporosis diagnosis but also offers a revealed feature map that can guide medical professionals for osteoporosis diagnosis. The innovation lies in a dual strategy: (i) a model integrating transfer learning from CNN architectures such as AlexNet, VGG-16, ResNet-50, VGG-19, InceptionNet, XceptionNet, and a custom CNN, and (ii) a dataset collection augmentation mechanism to enhance learning accuracy. The study includes binary and multiclass classification of knee joint X-ray images into normal, osteopenia, and osteoporosis groups, utilizing a dataset of 1947 knee X-rays for training and testing. Performance comparisons against state-of-the-art models reveal the proposed VGG-19 model achieves the highest accuracy at 92.0% for multiclass and 97.5% for binary. These findings underscore the potential of deep learning with transfer learning in aiding early osteoporosis detection, thereby mitigating fracture risks.

Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images

A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments. In this way, the work proposes a Squeeze-and-Excitation Attention-based ResNet50 (SEA-ResNet50) model for detecting COVID-19 utilizing chest X-ray data. Here, the idea lies in improving the residual units of ResNet50 using the squeeze-and-excitation attention mechanism. For further enhancement, the Ranger optimizer and adaptive Mish activation function are employed to improve the feature learning of the SEA-ResNet50 model. For evaluation, two publicly available COVID-19 radiographic datasets are utilized. The chest X-ray input images are augmented during experimentation for robust evaluation against four output classes namely normal, pneumonia, lung opacity, and COVID-19. Then a comparative study is done for the SEA-ResNet50 model against VGG-16, Xception, ResNet18, ResNet50, and DenseNet121 architectures. The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa validation scores of 0.975 (multiclass) and 0.98 (binary classification) over others. Furthermore, the visualization of the saliency maps of the abnormal regions is represented using the explainable artificial intelligence (XAI) model, thereby enhancing interpretability in disease diagnosis.

Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces a groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian cancer, integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, and ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, and overall robust performance across diverse evaluation metrics. The incorporation of a stacked model further enhances overall performance. Experimental results, conducted on the UBC-OCEAN training and testing datasets, highlight the proficiency of ViT models in accurately classifying ovarian cancer subtypes based on histopathological images. ViT-Large-P32-384 stands out as a top performer, achieving 98.79% accuracy during training and 97.37% during testing. Visualizations, including Receiver Operating Characteristic (ROC) curves and Local Interpretable Model-agnostic Explanations (LIME), provide insights into discriminative capabilities and enhance model interpretability. The proposed CAD framework represents a significant advancement in ovarian cancer diagnostics, offering a promising avenue for accurate and transparent multi-class classification of histopathological images.

Classification of Microorganism Using Convolutional Neural Network and H2O AutoML

The study advances microorganism image classification through a hybrid approach that integrates a Convolutional Neural Network (CNN), modified from the VGG19 architecture, with an ensemble model powered by H2O AutoML. Employing data augmentation and feature extraction, the approach enhances performance on a dataset encapsulating a broad spectrum of microorganism classes. The CNN model shows significant accuracy enhancements in complex bacteria classes, as depicted by the confusion matrix. Concurrently, the AutoML ensemble delivers comparable accuracy, notably in some classes where CNNs struggles. This research highlights the complementary strengths of deep learning and AutoML, demonstrating their impact in achieving high-precision microorganism recognition. Such advancements promise to significantly benefit bioinformatics and diagnostic applications, addressing the complexity of multi-class image classification tasks. The results indicate a successful combination of CNN and AutoML methodologies, setting a benchmark in automated microorganism classification, and also showcase the unique contributions and nuances of each method.

LW-XNet for segmentation and classification of skin lesions from dermoscopy images

The skin, one of the most crucial organs of the human body, serves as a barrier between the body and the external environment. Early detection of skin diseases is imperative to reduce mortality rates, as some untreated conditions may progress to skin cancer. Segmentation and classification of lesions represent pivotal and interrelated endeavors within the realm of skin disease diagnosis. For this purpose, this paper presents a comprehensive diagnostic framework for segmentation and classification of skin lesions, which integrates a Legendre multiwavelet transform-based fusion XNet (LW-XNet) with an improved soft attention dense connection convolutional network (ISA-DenseNet). LW-XNet combines the strengths of XNet in fusing different frequency components of images, along with the strong feature representation capability of LW bases with various regularities for overall contextual information and detailed information of dermoscopy images. Furthermore, its encoder devises a LWT channel concatenate (LCC) block to subdivide the image into eight wavelet coefficient feature images and perform concatenated processing on them, enabling it to better differentiate and comprehend the intricate features within dermoscopy images. Finally, ISA-DenseNet is utilized for multi-class classification of the segmented images. Experimental results demonstrate the superiority of the proposed framework over existing segmentation and classification methods.

Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification

By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.

Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar

BackgroundPreoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation–based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3. MethodsThe study included 1367 LM3 images from 784 patients who presented from 2021–2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3. ResultsThe ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels. ConclusionsThe development of a 3-phase computer-aided visualisation–based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.

A stochastic gradient method with variance control and variable learning rate for Deep Learning

In this paper we study a stochastic gradient algorithm which rules the increase of the mini-batch size in a predefined fashion and automatically adjusts the learning rate by means of a monotone or non-monotone line search procedure. The mini-batch size is incremented at a suitable a priori rate throughout the iterative process in order that the variance of the stochastic gradients is progressively reduced. The a priori rate is not subject to restrictive assumptions, allowing for the possibility of a slow increase in the mini-batch size. On the other hand, the learning rate can vary non-monotonically throughout the iterations, as long as it is appropriately bounded. Convergence results for the proposed method are provided for both convex and non-convex objective functions. Moreover it can be proved that the algorithm enjoys a global linear rate of convergence on strongly convex functions. The low per-iteration cost, the limited memory requirements and the robustness against the hyperparameters setting make the suggested approach well-suited for implementation within the deep learning framework, also for GPGPU-equipped architectures. Numerical results on training deep neural networks for multi-class image classification show a promising behaviour of the proposed scheme with respect to similar state of the art competitors.

An Empirical Study on Cataract Multiclass Grading Assessment with Slit Lamp Bio-microscope Images Using Neural Network Models

Cataract, an age-related eye disease, poses a significant ophthalmological public health challenge in both developed and developing nations. Tailoring treatment or surgery plans helps accurately categorise the cataract's developmental stage. Precise cataract grading helps in diagnosing cataracts and subsequently scheduling surgical intervention. In this project endeavour, a solution is presented to automate the cataract grading process utilizing slit lamp bio-microscope data sets acquired through smartphones. This innovation is particularly valuable for novice practitioners and non-specialist doctors/experts who may struggle with proficiently interpreting cataract progression, leading to potential misdiagnoses. To address this challenge, a Neural Network model is harnessed to automatically predict the grade of cataracts. The study employs multi-class image classification models, including the Convolutional neural network (CNN) model, the Efficient Net B0 model, and the ResNet50 model, for this purpose. Notably, the ResNet50 model outperforms the other models in terms of accuracy and prediction capability for the provided data set. Achieving an accuracy rate of 0.8611, the ResNet50 model demonstrates superior performance in classifying cataract grades, after augmenting the data set with 544 images. This performance comparison establishes the ResNet50 model as the most robust choice among the considered models and data sets.

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.