Framework For Multi-class Classification Research Articles

Empowering Brain Tumor Diagnosis through Explainable Deep Learning

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models.

Deep learning ensemble framework for multiclass diabetic retinopathy classification

Deep learning ensemble framework for multiclass diabetic retinopathy classification

DeLTran15: A Deep Lightweight Transformer-Based Framework for Multiclass Classification of Disaster Posts on X

DeLTran15: A Deep Lightweight Transformer-Based Framework for Multiclass Classification of Disaster Posts on X

SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm.

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top-bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.

Quantum-inspired algorithm for direct multi-class classification

Over the last few decades, quantum machine learning has emerged as a groundbreaking discipline. Harnessing the peculiarities of quantum computation for machine learning tasks offers promising advantages. Quantum-inspired machine learning has revealed how relevant benefits for machine learning problems can be obtained using the quantum information theory even without employing quantum computers. In the recent past, experiments have demonstrated how to design an algorithm for binary classification inspired by the method of quantum state discrimination, which exhibits high performance with respect to several standard classifiers. However, a generalization of this quantum-inspired binary classifier to a multi-class scenario remains nontrivial. Typically, a simple solution in machine learning decomposes multi-class classification into a combinatorial number of binary classifications, with a concomitant increase in computational resources. In this study, we introduce a quantum-inspired classifier that avoids this problem. Inspired by quantum state discrimination, our classifier performs multi-class classification directly without using binary classifiers. We first compared the performance of the quantum-inspired multi-class classifier with eleven standard classifiers. The comparison revealed an excellent performance of the quantum-inspired classifier. Comparing these results with those obtained using the decomposition in binary classifiers shows that our method improves the accuracy and reduces the time complexity. Therefore, the quantum-inspired machine learning algorithm proposed in this work is an effective and efficient framework for multi-class classification. Finally, although these advantages can be attained without employing any quantum component in the hardware, we discuss how it is possible to implement the model in quantum hardware.

A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set.

Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because of their large output size or cannot fully extract the deep semantic information between phrases and clauses. This paper proposes a multimodel-based deep learning framework for short-text multiclass classification with an imbalanced and extremely small dataset. Our framework mainly includes five layers: the encoder layer, the word-level LSTM network layer, the sentence-level LSTM network layer, the max-pooling layer, and the SoftMax layer. The encoder layer uses DistilBERT to obtain context-sensitive dynamic word vectors that are difficult to represent in traditional feature engineering methods. Since the transformer part of this layer is distilled, our framework is compressed. Then, we use the next two layers to extract deep semantic information. The output of the encoder layer is sent to a bidirectional LSTM network, and the feature matrix is extracted hierarchically through the LSTM at the word and sentence level to obtain the fine-grained semantic representation. After that, the max-pooling layer converts the feature matrix into a lower-dimensional matrix, preserving only the obvious features. Finally, the feature matrix is taken as the input of a fully connected SoftMax layer, which contains a function that can convert the predicted linear vector into the output value as the probability of the text in each classification. Extensive experiments on two public benchmarks demonstrate the effectiveness of our proposed approach on an extremely small dataset. It retains the state-of-the-art baseline performance in terms of precision, recall, accuracy, and F1 score, and through the model size, training time, and convergence epoch, we can conclude that our method can be deployed faster and lighter on mobile devices.

Cumulative dual-branch network framework for long-tailed multi-class classification

The long-tailed data distribution problem (i.e., a few classes account for majority data, while most classes account for minority data) is widespread in large-scale and real-world datasets, and it poses a huge challenge to the computer vision field. Existing methods of long-tailed classification mainly focus on re-sampling, re-weighting, and transfer learning. Although class imbalance learning can yield better long-tailed classification performance, the feature representative ability of the feature extraction network is damaged to a certain extent. To deal with these issues, the present work proposes a novel cumulative dual-branch network framework (CDBNF), which takes into account the class imbalance learning and feature representation learning at the same time by the dual-branch network architecture. In CDBNF, the class imbalance learning branch greatly improves the classification performance of tail classes, while the few-shot learning branch enhances the feature representative ability. Furthermore, a cumulative learning strategy (CLS) is proposed in CDBNF to make it pay more attention to the tail classes gradually in the training process. The effectiveness and practicability of the proposed CDBNF are verified by the four benchmark datasets. Experimental results show that the classification performance of CDBNF is superior to other state-of-the-art methods, while the intra-class feature variance is smaller than other state-of-the-art methods.

A two‐stream deep neural network‐based intelligent system for complex skin cancer types classification

Medical imaging systems installed in different hospitals and labs generate images in bulk, which could support medics to analyze infections or injuries. Manual inspection becomes difficult when there exist more images, therefore, intelligent systems are usually required for real-time diagnosis. Melanoma is one of the most common and severe forms of skin cancer that begins from the cells beneath the skin. Through dermoscopic images, it is possible to diagnose the infection at the early stages. In this regard, different approaches have been exploited for improved results. In this study, we propose a two-stream deep neural network information fusion framework for multiclass skin cancer classification. The proposed technique follows two streams: initially, a fusion-based contrast enhancement technique is proposed, which feeds enhanced images to the pretrained DenseNet201 architecture. The extracted features are later optimized using a skewness-controlled moth–flame optimization algorithm. In the second stream, deep features from the fine-tuned MobileNetV2 pretrained network are extracted and down-sampled using the proposed feature selection framework. Finally, most discriminant features from both networks are fused using a new parallel multimax coefficient correlation method. A multiclass extreme learning machine classifier is used to classify lesion images. The testing process is initiated on three imbalanced skin data sets—HAM10000, ISBI2018, and ISIC2019. The simulations are performed without performing any data augmentation step in achieving an accuracy of 96.5%, 98%, and 89%, respectively. A fair comparison with the existing techniques reveals the improved performance of our proposed algorithm.

Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification

A novel deep learning framework is proposed for lesion segmentation and classification. The proposed technique incorporates two primary phases. For lesion segmentation, Mask recurrent convolutional neural network (MASK R-CNN) based architecture is implemented. In this architecture, Resnet50 along with feature pyramid network (FPN) is utilized as a backbone. Later, fully connected layer-based features are mapped for the final mask generation. In the classification phase, 24-layered convolutional neural network architecture is designed, which performs activation based on the visualization of higher features. Finally, best CNN features are provided to softmax classifiers for final classification. Three datasets (i.e. PH2, ISBI2016, and ISIC2017) are utilized for the validation of the segmentation process, whilst HAM10000 dataset is utilized for the classification. From the results, it is concluded that the proposed method outperforms several existing techniques, based on the selected set of parameters including sensitivity (85.57%), precision (87.01%), F1- Score (86.28%), and accuracy (86.5%).

SliceNet-AD: slice selection-based convolution neural network model for classification of Alzheimer's disease

In recent days, a rapid advancement in imaging technologies has tremendously increased the collection of images in the medical field. These emerging technologies have also led the researchers to focus on computer aided diagnosis (CAD) using efficient machine learning and deep learning techniques. In this paper, we have proposed a framework for binary and multiclass classification of Alzheimer's disease (AD) using three-dimensional structural magnetic resonance images (sMRI) and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The collected images are subjected to pre-processing using FMRIB Software Library. After pre-processing, the three dimensional grey matter tissues are obtained as an output from tissue segmentation comprises of many two dimensional slices. But, processing and training all the slices requires a lot of computational time. Therefore our aim is to employ convolutional neural network only on the significant slices and also to report the performance of the model. Experimental results prove that the proposed slice selection classification framework achieves better performance when fused with clinical scores.

SliceNet-AD: slice selection-based convolution neural network model for classification of Alzheimer's disease

In recent days, a rapid advancement in imaging technologies has tremendously increased the collection of images in the medical field. These emerging technologies have also led the researchers to focus on computer aided diagnosis (CAD) using efficient machine learning and deep learning techniques. In this paper, we have proposed a framework for binary and multiclass classification of Alzheimer's disease (AD) using three-dimensional structural magnetic resonance images (sMRI) and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The collected images are subjected to pre-processing using FMRIB Software Library. After pre-processing, the three dimensional grey matter tissues are obtained as an output from tissue segmentation comprises of many two dimensional slices. But, processing and training all the slices requires a lot of computational time. Therefore our aim is to employ convolutional neural network only on the significant slices and also to report the performance of the model. Experimental results prove that the proposed slice selection classification framework achieves better performance when fused with clinical scores.

Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support

In this paper, we propose a deep convolutional neural network framework to classify dermoscopy images into seven classes. With taking the advantage that these classes can be merged into two (healthy/diseased) ones we can train a part of the network regarding this binary task only. Then, the confidences regarding the binary classification are used to tune the multi-class confidence values provided by the other part of the network, since the binary task can be solved more accurately. For both the classification tasks we used GoogLeNet Inception-v3, however, any CNN architectures could be applied for these purposes. The whole network is trained in the usual way, and as our experimental results on the skin lesion image classification show, the accuracy of the multi-class problem has been remarkably raised (by 7% considering the balanced multi-class accuracy) via embedding the more reliable binary classification outcomes.

Identification of Cognitive Learning Complexity of Assessment Questions Using Multi-class Text Classification

Cognitive learning complexity identification of assessment questions is an essential task in the domain of education, as it helps both the teacher and the learner to discover the thinking process required to answer a given question. Bloom’s Taxonomy cognitive levels are considered as a benchmark standard for the classification of cognitive thinking (learning complexity) in an educational environment. However, it was observed that some of the action verbs of Bloom’s Taxonomy are overlapping in multiple levels of the hierarchy, causing ambiguity about the real sense of cognition required. The paper describes two methodologies to automatically identify the cognitive learning complexity of given questions. The first methodology uses labelled Latent Dirichlet Allocation (LDA) as a machine learning approach. The second methodology uses the BERT framework for multi-class text classification for deep learning. The experiments were performed on an ensemble of 3000+ educational questions, which were based on previously published datasets along with the TREC question corpus and AI2 Biology How/Why question corpus datasets. The labelled LDA reached an accuracy of 83% while BERT based approach reached 89% accuracy. An analysis of both the results is shown, evaluating the significant factors responsible for determining cognitive knowledge.

Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

Multi-class sentiment classification has extensive application backgrounds, whereas studies on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment classification is proposed, which includes two parts: 1) selecting important features of texts using the feature selection algorithm, and 2) training multi-class sentiment classifier using the machine learning algorithm. Then, experiments are conducted for comparing the performances of four popular feature selection algorithms (document frequency, CHI statistics, information gain and gain ratio) and five popular machine learning algorithms (decision tree, naïve Bayes, support vector machine, radial basis function neural network and K-nearest neighbor) in multi-class sentiment classification. The experiments are conducted on three public datasets which include twelve data subsets, and 10-fold cross validation is used to obtain the classification accuracy concerning each combination of feature selection algorithm, machine learning algorithm, feature set size and data subset. Based on the obtained 3600 classification accuracies (4 feature selection algorithms × 5 machine learning algorithms × 15 feature set sizes × 12 data subsets), the average classification accuracy of each algorithm is calculated, and the Wilcoxon test is used to verify the existence of significant difference between different algorithms in multi-class sentiment classification. The results show that, in terms of classification accuracy, gain ratio performs best among the four feature selection algorithms and support vector machine performs best among the five machine learning algorithms. In terms of execution time, the similar comparisons are also conducted. The obtained results would be valuable for further improving the existing multi-class sentiment classifiers and developing new multi-class sentiment classifiers.

Attribute-based Decision Graphs: A framework for multiclass data classification

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph–AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes.

Iterative ensemble feature selection for multiclass classification of imbalanced microarray data.

BackgroundMicroarray technology allows biologists to monitor expression levels of thousands of genes among various tumor tissues. Identifying relevant genes for sample classification of various tumor types is beneficial to clinical studies. One of the most widely used classification strategies for multiclass classification data is the One-Versus-All (OVA) schema that divides the original problem into multiple binary classification of one class against the rest. Nevertheless, multiclass microarray data tend to suffer from imbalanced class distribution between majority and minority classes, which inevitably deteriorates the performance of the OVA classification.ResultsIn this study, we propose a novel iterative ensemble feature selection (IEFS) framework for multiclass classification of imbalanced microarray data. In particular, filter feature selection and balanced sampling are performed iteratively and alternatively to boost the performance of each binary classification in the OVA schema. The proposed framework is tested and compared with other representative state-of-the-art filter feature selection methods using six benchmark multiclass microarray data sets. The experimental results show that IEFS framework provides superior or comparable performance to the other methods in terms of both classification accuracy and area under receiver operating characteristic curve. The more number of classes the data have, the better performance of IEFS framework achieves.ConclusionsBalanced sampling and feature selection together work well in improving the performance of multiclass classification of imbalanced microarray data. The IEFS framework is readily applicable to other biological data analysis tasks facing the same problem.

Multi-class Support Vector Machine classifiers using intrinsic and penalty graphs

In this paper, a new multi-class classification framework incorporating geometric data relationships described in both intrinsic and penalty graphs in multi-class Support Vector Machine is proposed. Direct solutions are derived for the proposed optimization problem in both the input and arbitrary-dimensional Hilbert spaces for linear and non-linear multi-class classification, respectively. In addition, it is shown that the proposed approach constitutes a general framework for SVM-based multi-class classification exploiting geometric data relationships, which includes several SVM-based classification schemes as special cases. The power of the proposed approach is demonstrated in the problem of human action recognition in unconstrained environments, as well as in facial image and standard classification problems. Experiments indicate that by exploiting geometric data relationships described in both intrinsic and penalty graphs the SVM classification performance can be enhanced.

Beyond bag of latent topics: spatial pyramid matching for scene category recognition

We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

An unsupervised discriminative extreme learning machine and its applications to data clustering

Extreme Learning Machine (ELM), which was initially proposed for training single-layer feed-forward networks (SLFNs), provides us a unified efficient and effective framework for regression and multiclass classification. Though various ELM variants were proposed in recent years, most of them focused on the supervised learning scenario while little effort was made to extend it into unsupervised learning paradigm. Therefore, it is of great significance to put ELM into learning tasks with only unlabeled data. One popular approach for mining knowledge from unlabeled data is based on the manifold assumption, which exploits the geometrical structure of data by assuming that nearby points will also be close to each other in transformation space. However, considering the manifold information only is insufficient for discriminative tasks. In this paper, we propose an improved unsupervised discriminative ELM (UDELM) model, whose main advantage is to combine the local manifold learning with global discriminative learning together. UDELM can be efficiently optimized by solving a generalized eigen-value decomposition problem. Extensive comparisons over several state-of-the-art models on clustering image and emotional EEG data demonstrate the efficacy of UDELM.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.