Image Classification Algorithm Research Articles

Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation

As an effective computer-aided diagnostic tool, deep learning has been successfully applied to the classification of breast histopathological images. However, the performance of the deep model is data-driven, and it is difficult to obtain satisfied results when the number of histopathological images is small and labelling histopathological images is difficult. Moreover, in traditional deep learning methods, the representation of features is monotonous, which leads to the limitation of the classification performance of the model. This study proposes an auto-encoder reconstructed semi-supervised domain adaptation for a breast histopathological image classification algorithm. First, the model was pre-trained and transferred to extract high-level features of the sample images. Then, the encoding and decoding parts of the auto-encoder were used to reconstruct the feature representation learning and the sample feature reconstruction learning, respectively. This ensured that the useful information for the classification was purified and retained. At the same time, the domain discriminator was used to confuse the source and target domain features to enhance the learning ability of the model. Finally, the distribution difference of features at different depths of the auto-encoder was measured to minimize the discrepancy of feature distribution between domains, so as to complete the classification of histopathological images. Compared to the results of the comparative and ablation algorithms from the BreakHis to SNL datasets, the proposed method achieved the best results in terms of F1 score (93.40%), accuracy (95.24%), sensitivity (94.66%), and specificity (95.56%). The experimental results demonstrate that the proposed method achieves a remarkable classification performance.

Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.

Finger Vein Detection using Deep Learning

The deep learning is a part of machine learning that uses neural networks with many layers (deep architecture) to fit the intricate patterns. Deep learning has shown remarkable performance in image classification mainly due to its ability in learning and extracting features from the raw inputs. In this research study, some techniques of image preprocessing were described and utilized to machine learning algorithm for image classification. The purpose was to promote the specific preprocessing techniques on the impact, which are commonly used in classification model of images, relating to time and precision improvement. The techniques that are applied in this documentation include; image enhancement, binarization, transformation, edge detection and scaling. The present research study therefore comes handy in trying to establish the significance of enhancing statistic and the impact on the performance of the model. The following preprocessing methods have been used because the image datasets need preprocessing to be input for the machine learning models for better image classification

Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer

The aim of this study was to explore the application of computational models for the analysis of histopathological images in the context of colon cancer. A comprehensive dataset of colon cancer images annotated into eight distinct categories based on their representation of cancerous cell portions was used. The primary objective was to employ various image classification algorithms to assess their efficacy in the context of cancer classification. Additionally, this study investigated the use of feature extraction techniques to derive meaningful data from the images, contributing to a more nuanced understanding of cancerous tissues, comparing the performance of different image classification algorithms in the context of colon cancer image analysis. The findings of this research suggested that XGboost provides the highest accuracy (89.79%) and could contribute to the growing body of knowledge in computational pathology. Other algorithms, such as the random forest, SVM, and CNN, also provided satisfactory results, offering insights into the effectiveness of image classification algorithms in distinguishing between different categories of cancerous cells. This work holds implications for the development of more accurate and efficient tools, underscoring the potential of computational models in enhancing the analysis of histopathological images and improving diagnostic capabilities in cancer research.

Enhancing Sustainable Automated Fruit Sorting: Hyperspectral Analysis and Machine Learning Algorithms

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits.

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.

Optical light scattering to improve image classification via wavelength division multiplexing

Machine learning is constantly contributing significant progress in many areas while posing huge demands for computing resources. It has been demonstrated the feasibility of leveraging random light scattering to decrease the computational resource demands of image classification algorithms. However, optical devices in optical random scattering systems, such as cameras, constrain the bandwidth of the entire system. In this study, a high-speed scattering system based on wavelength division multiplexing (WDM) was proposed. By employing the high bandwidth semiconductor lasers and quadrant PIN detectors, this WDM scattering system achieves over a 1000-times increase in acquisition speed compared to the traditional camera-based spatial scattering system. Moreover, this WDM scattering system has been demonstrated to improve the classification accuracy for RC on nine datasets, including MNIST, Chest_X-ray, and Malaria, by 26.15%.

Development of a Hybrid Medical Softbot for Enhanced Decision Making using AI-tools

Health is wealth. The maintenance of health is of paramount importance. Due to increasing environmental decay, human health is threatened and therefore requires maintenance. Healthcare providers are few in number and therefore may not be able to cater for everyone. They tend to get fatigued because of the volume of work required on daily basis. This terribly affects their decision making and may lead to death as a result of wrong diagnosis or recommendations. This is the motivation behind this work; design and implementation of a hybrid medical softbot for enhanced decision making. This work was designed with the aid of machine learning algorithms for image analysis and classification and a rule based system that accepts input in the form of symptoms from user to make expert diagnosis and recommendation. Object oriented analysis and design methodology was employed in the analysis and design phase. The result is a hybrid softbot capable of analyzing and classifying X-ray images and giving expert diagnosis for patients.

Toward Automated Small Bowel Capsule Endoscopy Reporting using a Summarizing Machine learning Algorithm: The SUM UP study

Background and objectivesDeep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1) or low (P0) bleeding potential, while dramatically decreasing the reading time. We aimed to improve the performance of a DL algorithm by characterizing vascular abnormalities using a machine learning (ML) classifier, and selecting the most relevant images for insertion into reports. Materials and methodsA training dataset of 75 SB CE videos was created, containing 401 sequences of interest that encompassed 1,525 images of various vascular lesions. Several image classification algorithms were tested, to discriminate "typical angiodysplasia" (P2/P1) and "other vascular lesion" (P0) and to select the most relevant image within sequences with repetitive images. The performances of the best-fitting algorithms were subsequently assessed on an independent test dataset of 73 full-length SB CE video recordings. ResultsFollowing DL detection, a random forest (RF) method demonstrated a specificity of 91.1%, an area under the receiving operating characteristic curve of 0.873, and an accuracy of 84.2% for discriminating P2/P1 from P0 lesions while allowing an 83.2% reduction in the number of reported images. In the independent testing database, after RF was applied, the output number decreased by 91.6%, from 216 (IQR 108-432) to 12 (IQR 5-33). The RF algorithm achieved 98% agreement with initial, conventional (human) reporting. Following DL detection, the RF method allowed better characterization and accurate selection of images of relevant (P2/P1) SB vascular abnormalities for CE reporting without impairing diagnostic accuracy. These findings pave the way for automated SB CE reporting.

A balanced sample generation method for semi-supervised carotid ultrasound image classification via pseudo-label distribution and model confidence

Abstract The rupture of carotid plaques dramatically increases the risk of stroke, making it essential to classify these plaques to promptly identify high-risk cases and assist in patient management. Deep learning-based plaque classification methods need large labeled datasets, which are often challenging to acquire. Semi-supervised learning offers a solution by enabling deep learning networks to perform well with fewer labeled images. However, this method filters pseudo-labels based on thresholds, resulting in imbalanced quantities and cognitive bias in the network. To tackle this issue, we present a Balanced Sample Generation based Pseudo-label algorithm (BSGP) for semi-supervised carotid ultrasound image classification. BSGP generates new samples based on pseudo-label distribution and model confidence in each category, reducing cognitive bias from imbalanced pseudo-label quantities. Evaluated on 1,270 ultrasound carotid plaque images, our BSGP outperformed other semi-supervised methods (FixMatch, FlexMatch, AdaMatch, FreeMatch) when using 10%, 30%, and 50% labeled data. This illustrates the efficacy of our method in leveraging unlabeled images for model training, potentially advancing the use of deep learning in ultrasound-based plaque classification and supporting clinical diagnoses.

Machine Learning Algorithms for Image Classification: A Comparative Review

In the realm of computer vision, one of the most important challenges is image categorization. Its goal is to assign semantic labels to photographs based on a predetermined set of categories, and it does this via a process called semantic labeling. In attempt to find a solution to this issue, several distinct machine learning algorithms have been developed throughout the course of time; each strategy has its own unique mix of benefits and drawbacks. This article gives an in-depth review and comparison of a wide variety of well-known machine learning approaches for the classification of pictures. This review covers a wide range of algorithms, including more traditional approaches such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Transfer Learning using pretrained models such as VGG, Reset, and Inception.

An improved automatic image labeling and classification algorithm for multi-mode damage quantification of 2.5D woven composites based on deep learning strategy

Accurately identifying and quantifying the complex multi-mode damages in woven composites is of vital importance to evaluate the service life and improve reliability of the components. However, the current advanced methods based on the deep learning framework remain mainly the manual labeling, resulting in unclear fiber/resin interfaces, easily-overlooked microcracks, and lower efficiency. To overcome the problem, this paper proposes an improved automatic image labeling and classification algorithm based on deep learning strategy to quantify the uncertainty damages of 2.5D woven composites. In detail, the original micro-computed tomography (CT) images are automatically labeled by an image algorithm that utilizes grayscale values and image boundaries to produce image datasets. Subsequently, the DCNN model is trained using the image datasets. Then, the trained deep convolutional neural networks (DCNN) model is used to identify unseen CT images and separate the damage and different sub-phases of 2.5D woven composites. Finally, the connected component analysis is introduced to classify the global cracks at the meso-scale. The results show that the proposed automatic image labeling and classification algorithm can achieve a damage identification precision of 85.87 %, surpassing that of other models. Moreover, the multi-mode damages of 2.5D woven composites are accurately captured. In the warp direction, the bending damage accumulation predominantly manifests as interface debonding, representing 51.93 % of the damage percentage. In the weft direction, it is primarily characterized by matrix cracking, representing 60.98 % of the damage percentage. It is expected that the study can provide data support for the application of large-scale and complex structural components.

Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction

A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3DCNN), this algorithm enables the classification model to have good tolerance for various types of noise by using a Minimum Noise Fraction (MNF) as dimensionality reduction module for hyperspectral image input data. In addition, by introducing lightweight hybrid attention modules with the spatial and the channel information, the deep features extracted by the Convolutional Neural Network are further refined, ensuring that the model has high classification accuracy. Public dataset experiments have shown that compared to traditional methods, the MNF in this algorithm reduces the dimensionality of input spectral data, preserves information with higher signal-to-noise ratio(SNR) in the spectral bands, and aggregates spectral features into class feature vectors, greatly improving the noise robustness of the model. At the same time, based on a lightweight spectral–spatial hybrid attention mechanism, combined with fewer spectral dimensions, the model effectively avoids overfitting. With less loss in model training speed, it achieved better classification accuracy in small-scale training sample experiments, fully demonstrating the good generalization ability of this algorithm.

A novel embedded kernel CNN-PCFF algorithm for breast cancer pathological image classification

Early screening of breast cancer through image recognition technology can significantly increase the survival rate of patients. Therefore, breast cancer pathological image is of great significance for medical diagnosis and clinical research. In recent years, numerous deep learning models have been applied to breast cancer image classification, with deep CNN being a typical representative. Due to the use of multi-depth small convolutional kernels in mainstream CNN architectures such as VGG and Inception, the obtained image features often have high dimensionality. Although high dimensionality can bring more fine-grained features, it also increases the computational complexity of subsequent classifiers and may even lead to the curse of dimensionality and overfitting. To address these issues, a novel embedded kernel CNN principal component feature fusion (CNN-PCFF) algorithm is proposed. The constructed kernel function is embedded in the principal component analysis to form the multi-kernel principal component. Multi-kernel principal component analysis is used to fuse the high dimensional features obtained from the convolution base into some representative comprehensive variables, which are called kernel principal components, so as to achieve the purpose of dimensionality reduction. Any type of classifier can be added based on multi-kernel principal components. Through experimental analysis on two public breast cancer image datasets, the results show that the proposed algorithm can improve the performance of the current mainstream CNN architecture and subsequent classifiers. Therefore, the proposed algorithm in this paper is an effective tool for the classification of breast cancer pathological images.

Imbalanced image classification algorithm based on fine-grained analysis

Fine-grained attribute analysis and data imbalance have always been research hotspots in the field of computer vision. Due to the complexity and diversity of fine-grained attribute images, traditional image classification methods have shortcomings in paying attention to fine-grained attributes of images and perform poorly when dealing with imbalanced data sets. To overcome these problems, this study proposes a fine-grained image threshold classification algorithm based on deep metric learning. By introducing a metric learning method, the focus on fine-grained attributes of images is enhanced. At the same time, by applying pairwise loss and proxy loss, the classification accuracy of the model is improved and the model convergence speed is accelerated. In order to deal with the problem of data imbalance, a classifier based on threshold analysis is designed. The classifier uses threshold analysis technology to achieve multi-level classification of fine-grained images, thereby improving the problem of low classification accuracy of a few categories in imbalanced data sets. Experimental results show that the proposed fine-grained image threshold classification algorithm based on deep metric learning is significantly superior to other methods in terms of classification accuracy.

A fine-grained image classification algorithm based on self-supervised learning and multi-feature fusion of blood cells

Leukemia is a prevalent and widespread blood disease, and its early diagnosis is crucial for effective patient treatment. Diagnosing leukemia types heavily relies on pathologists’ morphological examination of blood cell images. However, this process is tedious and time-consuming, and the diagnostic results are subjective, leading to potential misdiagnosis and underdiagnosis. This paper proposes a blood cell image classification method that combines MAE with an enhanced Vision Transformer to tackle these challenges. Initially, pre-training occurs on two datasets, TMAMD and Red4, using the MAE self-supervised learning algorithm. Subsequently, the pre-training weights are transferred to our improved model.This paper introduces feature fusion of the outputs from each layer of the Transformer encoder to maximize the utilization of features extracted from lower layers, such as color, contour, and texture of blood cells, along with deeper semantic features. Furthermore, the dynamic margins for the subcenter Arcface Loss function are employed to enhance the model’s fine-grained feature representation by achieving inter-class dispersion and intra-class aggregation. Models trained using our method achieved state-of-the-art results on both the TMAMD dataset and Red4 dataset, with classification accuracies of 93.51% and 81.41%, respectively. This achievement is expected to be a valuable reference for physicians in their clinical diagnoses.

Research on the improvement of image classification algorithm based on incremental learning

In the field of machine learning, incremental learning methods allow models to learn new tasks without forgetting the old ones, but the catastrophic forgetting problem often encountered in this process greatly limits their applications. In this paper, an improved algorithm EPASS (Enhanced Prototype Augmentation and Self-Supervision) is proposed for incremental learning in image classification tasks, which effectively alleviates the limitations of the traditional PASS algorithm and the EWC algorithm in terms of catastrophic forgetting, sensitivity of parameter selection, and computational cost. The EPASS algorithm improves the flexibility and adaptability of the model by automatically selecting which prototypes to keep for different tasks through a dynamic prototype storage strategy. Meanwhile, the EWC-based prototype update strategy allows prototypes to be updated as new data arrives, further improving the model's adaptability to new tasks while reducing computational cost. Experimental results on the CIFAR-100 dataset show that the EPASS algorithm outperforms the PASS algorithm to some extent in terms of accuracy at all stages. These results validate the effectiveness of the EPASS algorithm in mitigating the catastrophic forgetting problem and provide new directions for future incremental learning research.

Unfolding multilevel agglomerative strategies for SVM classification: a case study in discriminating spectrally similar land covers

ABSTRACT In remote sensing applications, image classification algorithms normally require parameter optimization strategies to adapt to the complexities of the data and determine the model’s parameters for optimal performance. However, a unique set of hyper-parameters is generally chosen, without acknowledging the presence of subsets of classes that exhibit different levels of spectral similarity. A model used to distinguish between well-separable classes might not be optimal for delineating other classes. This study explores a classification method that extends the standard One-Against-One (OAO) and One-Against-All (OAA) strategies for Support Vector Machines (SVMs), based on agglomerative hierarchical clustering of spectrally-similar classes. The main objective is to provide additional classification scenarios where more detailed per-class analyses can be performed, rather than simple comparisons of global accuracies, particularly for hard-to-distinguish classes. Clusters of classes closely located in the feature space are initially classified using an OAA strategy, while each cluster undergoes further subdivision using an OAO approach. This strategy aims to identify the most effective sequence for incorporating subgroups of classes into the tuning process. The experimental results indicate that the proposed method can be efficiently applied to classify remote sensing data, producing varying levels of enhancement in per-class accuracies at each stage of the agglomerative process.

Deep‐Learning‐Assisted Affinity Classification for Humoral Immunoprotein Complexes

Immunoglobulins are important building blocks in biology and biotechnology. With the emergence of comprehensive deep‐learning approaches, there are enormous opportunities for fast and accurate methods of classification of protein–protein interactions to arise. Herein, widely accessible image classification algorithms for species‐specific typification of a range of different immunoglobulin G (IgG) complexes are repurposed. Droplets of various immunoglobulins mixed with a B‐cell superantigen (SAg) (recombinant staphylococcal Protein A) are deposited onto hydrophobic polymer substrates and the resulting protein stains are imaged using polarized light microscopy. A comprehensive study based on 23 745 images finds that the pretrained convolutional neural network (CNN) InceptionV3 not only successfully categorizes IgGs from four different species but also predicts their binding affinity to Protein A: averaged over 36 binding pairs, the following are observed: 1) an overall accuracy of 81.4%, 2) the highest prediction accuracy for human IgG, the antibody with the highest binding affinity for Protein A, and 3) that the classification accuracy regarding the various IgG/Protein A ratios generally correlates with the binding strength of the protein–protein–complex as determined via circular dichroism spectroscopy. In addition, the CNN pretrained with IgG/Protein A stain images has been challenged with a new set of images using a different superantigen (SAg, Protein G). Despite the use of the unknown SAg, the CNN correctly classifies the images of human IgG and Protein G as indicated by a 94% accuracy over the various molar binding ratios. These findings are noteworthy because they demonstrate that appropriately pretrained CNNs can be used for the prediction of protein–protein interactions beyond the scope of the original training set. Aided by deep‐learning methods, simple stains of mixed protein solutions may serve as accurate predictors of the strength of protein–protein interactions with relevance to protein engineering, self‐aggregation, or protein stability in complex media.

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases.