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Automatic detection of Diabetic Retinopathy (DR) is critically important, as it is the primary reason of irreversible loss of vision in the economically active populations in the developed countries. Early detection of the onset of Diabetic Retinopathy can greatly benefit clinical treatment; although several different feature extraction methods have been proposed, the task of retinal image classification remains tedious even for trained clinicians. This paper emphasizes on Diabetic Retinopathy detection as well as the analysis of the different stages of DR, performed on fundus images using Deep Learning algorithms. Fundus images of the patient were provided as input to the developed model evaluated using the real-time dataset of the hospital. The proposed ResNet-18 architecture with swish function has achieved an accuracy of 93.51%, sensitivity of 93.42%, precision of 93.77% and F1-score of 93.59%. The paper concludes with a comparative study of Simple CNN, VGGNet-16, MobileNet-V2 and ResNet architectures and other state-of-art approaches, which highlights ResNet-18 with Swish as the most effective deep learning classifier model for DR detection. • Deep learning outperforms traditional screening for Diabetic Retinopathy, reducing ophthalmologists' workload. • ResNet18's residual connections mitigate the vanishing gradient problem and enable effective retinal image pattern learning. • Swish activation in ResNet18 enhances Diabetic Retinopathy detection with improved accuracy and faster training convergence. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep learning (DL) models have been extensively explored for the early diagnosis of various cancer diseases, including Leukemia, with many of them achieving significant performance improvements comparable to those of human experts. However, challenges like limited image data, inaccurate annotations, and prediction reliability still hinder their broad implementation to establish a trustworthy computer-aided diagnosis (CAD) system. This paper introduces a novel weighted-average ensemble model for classifying Acute Lymphoblastic Leukemia, along with a reliable Computer-Aided Diagnosis (CAD) system that combines the strengths of both ML and DL approaches. Initially, a variety of filtering methods are extensively analyzed to determine the most suitable image representation, with subsequent data augmentation techniques to expand the training data. Second, a modified VGG-19 model was proposed with fine-tuning that was utilized as a feature extractor to extract meaningful features from the training samples. Third, A small-loss approach and probabilistic local outlier factor (pLOF) have been developed on the extracted features to address the label noise issue. Fourth, we proposed an weighted-average ensemble model based on the top five models as base learners, with weights calculated based on their model uncertainty to ensure reliable predictions. Fifth, we calculated Shapley values based on cooperative game theory and performed feature selection with different feature combinations to determine the optimal number of features using SHAP. Finally, we integrate these strategies to develop an interpretable CAD system. This system not only predicts the disease but also generates Grad-CAM images to visualize potential affected areas, enhancing both clarity and diagnostic insight. All of our code is provided in the following repository: https://github.com/taareek/leukemia-classification [ABSTRACT FROM AUTHOR]

Motivated by researching new image texture modeling that improves state-of-the-art LBP variants and non-LBP descriptors, this paper proposes a novel approach for constructing local image descriptors, which are suitable for histogram based image representation. Instead of heuristic code constructions, the proposed approach is based on local concave-and-convex characteristics, which have high ability to extract discriminative and stable texture representation. Different from the majority of descriptors that only encode relationships between the pixels in doublets around central pixel (within 3  ×  3 neighborhood), the proposed approach encodes relationships between the pixels in triplets by including the central pixel in the modeling. We build two distinct descriptors by dividing local features into two distinct groups, i.e., local concave and convex microstructure patterns (LCvMSP and LCxMSP), according to relationships between the pixels inside the triplets, formed along closed path around the central pixel of a 3 × 3-grayscale image patch. To make the descriptors more insensitive to noise and invariant to monotonic gray scale transformation, two supplementary triplets are added in the modeling. These triplets are formed using the central pixel and four virtual pixels set to the median of the grey-scale values of the 3 × 3 neighbourhood and the whole image and the average local and global gray levels respectively. The histograms obtained from the single scale descriptors LCvMSP and LCxMSP are concatenated together to build multi-scale histogram feature vector referred to as local concave-and-convex micro-structure pattern (LCCMSP), that is expected to better represent salient local texture structure. We evaluated the effectiveness of the proposed methods on thirteen challenging representative widely-used texture datasets, and found that the proposed LCvMSP, LCxMSP and LCCMSP operators achieve performances that are competitive or better than a large number of recent most promising state-of- the-art LBP variants and non-LBP descriptors. Statistical comparison based on Wilcoxon signed rank test demonstrated that the proposed methods are the top three over all the tested datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework for defining feature extraction techniques, called Pixel Clustering. It is an extension of feature extraction with Wavelets. We propose two linear feature extraction techniques using Pixel Clustering: IntensityPatches and RegionPatches. We assess the methods in color and grayscale image datasets: two face datasets and two object datasets. The proposed methods present a short computation time for feature extraction and high accuracy compared with linear feature extraction methods and other state-of-the-art feature extraction techniques. [ABSTRACT FROM AUTHOR]

Mechanical damage detection on waxy maize is critical to guarantee maize quality and meet the product requirements. A novel spectral–spatial feature extraction enhanced fully connected neural network (SSFE-FCNN) classification method is proposed in this paper to achieve pixel-wise mechanical damage detection of waxy maize using the hyperspectral image (HSI). The proposed SSFE-FCNN utilizes the online tensor dictionary learning algorithm to extract spectral–spatial tensor features from HSI cubes. Moreover, the designed fully connected neural network extracts high-level features from the tensor features and provides classification results. The SSFE-FCNN combines the advantages of sparse tensor representation and neural networks to exploit joint spectral–spatial information in HSI and improve detection performance. Experimental results on the HSI of waxy maize show that the proposed SSFE-FCNN is superior to the support vector machine and the currently popular neural network methods. The overall accuracy of SSFE-FCNN in the pixel-wise classification of waxy maize achieves as high as 98.09%. The false and missing detection rates are 2.08% and 1.96%, respectively. Furthermore, pixel-wise detection can detect slight damage and perform the maize grading operation according to the damage ratio. • An SSFE-FCNN classification method for the hyperspectral images of waxy maize • SSFE-FCNN extracts joint spectral–spatial features to improve detection performance • SSFE-FCNN combines the advantages of sparse tensor representation and neural networks • The pixel-wise detection can detect subtle damage and visualize the mechanical damage • The detection performance of SVM and two transformer network methods are compared [ABSTRACT FROM AUTHOR]

The human visual system proves expert in discovering patterns in both global and local feature space. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel spatial pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling ( R 2 FP ), to better extract rich and robust representation from sparse feature maps learned from the raw data. Both local and global pooling strategies are further considered to instantiate such a method. The former selects the most representative features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balance kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

When handling pattern classification problem such as face recognition and digital handwriting identification, image data is always represented to high dimensional vectors, from which discriminant features are extracted using dimensionality reduction methods. So in this paper, we present a supervised manifold learning based dimensionality reduction method named constrained discirminant neighborhood embedding (CDNE). In the proposed CDNE, on one hand, the class information of samples is taken into account to construct both an inter-class graph and an intra-class graph, where neighborhood points in the intra-class graph are selected from those with the same class and any point in the inter-class graph should sample those labeled different classes as its neighborhood points. On the other hand, locally least linear reconstruction technique is also introduced to model an objective function under the local uncorrelation constraint to explore a discriminant subspace. Compared to some related and state-of-the-art dimensionality reduction methods such as discriminant neighborhood embedding (DNE), supervised locality discriminant manifold learning (SLDML), discriminant sparse neighborhood preserving embedding (DSNPE), local graph embedding based on maximum margin criterion (LGE/MMC), uncorrelated discriminant locality preserving projection (UDLPP) and locally uncorrelated discriminant projection (LUDP), the proposed CDNE has been validated to be efficient and feasible by experimental results on some benchmark face data sets including CMU PIE, ORL and FERET. [ABSTRACT FROM AUTHOR]

Abstract: Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. [Copyright &y& Elsevier]

Abstract: Empirically, we find that despite the most exclusively discriminative features owned by one specific object category, the various classes of objects usually share some common patterns, which do not contribute to the discrimination of them. Concentrating on this observation and motivated by the success of dictionary learning (DL) framework, in this paper, we propose to explicitly learn a class-specific dictionary (called particularity) for each category that captures the most discriminative features of this category, and simultaneously learn a common pattern pool (called commonality), whose atoms are shared by all the categories and only contribute to representation of the data rather than discrimination. In this way, the particularity differentiates the categories while the commonality provides the essential reconstruction for the objects. Thus, we can simply adopt a reconstruction-based scheme for classification. By reviewing the existing DL-based classification methods, we can see that our approach simultaneously learns a classification-oriented dictionary and drives the sparse coefficients as discriminative as possible. In this way, the proposed method will achieve better classification performance. To evaluate our method, we extensively conduct experiments both on synthetic data and real-world benchmarks in comparison with the existing DL-based classification algorithms, and the experimental results demonstrate the effectiveness of our method. [Copyright &y& Elsevier]