The performance of Adaboost is highly sensitive to noisy and outlier samples. This is therefore the weights of these samples are exponentially increased in successive rounds. In this paper, three novel schemes are proposed to hunt the corrupted samples and eliminate them through the training process. The methods are: I) a hybrid method based on K-means clustering and K-nearest neighbor, II) a two-layer Adaboost, and III) soft margin support vector machines. All of these solutions are compared to the standard Adaboost on thirteen Gunnar Raetsch’s datasets under three levels of class-label noise. To test the proposed method on a real application, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched control subjects, are recorded via 20 channels in the idle state. Several features including autoregressive coefficients, band power and fractal dimension are extracted from EEG signals of all participants. Sequential feature subset selection technique is adopted to select the discriminative EEG features. Experimental results imply that exploiting the proposed hunting techniques enhance the Adaboost performance as well as alleviating its robustness against unconfident and noisy samples over Raetsch benchmark and EEG features of the two groups.

Abstract This research investigates advanced approaches in medical image analysis, specifically focusing on segmentation and classification techniques, as well as their integration into multi‐task architectures for lung infections. This research begins by explaining key architectural models used in segmentation and classification tasks. The study extends to the enhancement of these architectures through attention modules and conditional random fields. Relevant datasets and evaluation metrics, incorporating discussions on loss functions are also reviewed. This review encompasses recent advancements in single‐task and multi‐task models, highlighting innovations in semi‐supervised, self‐supervised, few‐shot, and zero‐shot learning techniques. Empirical analysis is conducted on both single‐task and multi‐task architectures, predominantly utilizing the U‐Net framework, and is applied across multiple datasets for segmentation and classification tasks. Results demonstrate the effectiveness of these models and provide insights into the strengths and limitations of different approaches. This research contributes to improved detection and diagnosis of lung infections by offering a comprehensive overview of current methodologies and their practical applications.

Abstract Covid‐19 caused by the SARS‐CoV2 virus has become a pandemic all over the world. By growing in a number of cases, there is a need for clinical decision‐making system based on machine learning models. Most of the previous studies have examined only one task, while the detection and identification of infectious area are conducted simultaneously in the real world. Thus, the present study aims to propose a multi‐task model which can perform automatic classification‐segmentation for screening Covid‐19 pneumonia by using chest CT imaging. This model includes a common encoder for feature representation, one decoder for segmentation, and a multi‐layer perceptron for classification, respectively. The proposed model can evaluate three datasets, along with the effect of images size on the output of the model. The outputs were examined in both multi‐task and single‐task learning. The result indicates that the effect of multi‐task is significant in improving the results, which can increase the outputs of each task performance to 95.40% accuracy in classification and 95.40% in segmentation. Further, the model represented the highest results among the state‐of‐the‐art methods. The proposed model can be applied as a primary screening tool to help primary service staff in better referral of the suspected patients to specialists.

Abstract COVID‐19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multi‐task architecture that addresses the classification and segmentation tasks for COVID‐19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multi‐layer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CT‐scan images undergo pre‐processing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter pre‐processing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology pre‐processing, exhibits excellent performance with high accuracy, low mean squared error, high peak signal‐to‐noise ratio, high structural similarity index, and a mean dice coefficient of 88.86 ± 0.05 for dataset 1, and 87.97 ± 0.02 for dataset 2. Furthermore, the pre‐trained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multi‐task model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of pre‐processing algorithms and the multi‐task framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment.

Ensemble learners and deep neural networks are state-of-the-art schemes for classification applications. However, deep networks suffer from complex structure, need large amount of samples and also require plenty of time to be converged. In contrast, ensemble learners (especially AdaBoost) are fast to be trained, can work with small and large datasets and also benefit strong mathematical background. In this paper, we have developed a new orthogonal version of AdaBoost, termed as ORBoost, in order to desensitize its performance against noisy samples as well as exploiting low number of weak learners. In ORBoost, after reweighting the distribution of each learner, the Gram-Schmidt rule updates those weights to make a new samples’ distribution to be orthogonal to the former distributions. In contrast in AdaBoost, there is no orthogonality constraint even between two successive weak learners while there is a similarity between the distributions of samples in different learners. To assess the performance of ORBoost, 16 UCI-Repository datasets along with six big datasets are deployed. The performance of ORBoost is compared to the standard AdaBoost, LogitBoost and AveBoost-II over the selected datasets. The achieved results support the significant superiority of ORBoost to the counterparts in terms of accuracy, robustness, number of exploited weak learners and generalization on most of the datasets.

This paper automatizes the process of bone age maturity assessment by applying three versions of particle swarm optimization (PSO) along with image processing methods to the left hand X‐ray images. PSO versions were adopted to enhance the segmentation accuracy. The proposed method was compared to the conventional visual inspection method in terms of three segmentation criteria, classification accuracy, robustness against noise and computational complexity. Herein, PSO, worst behavior‐based PSO (WB‐PSO) and adaptive inertia weight (AIW‐PSO) along with Otsu and an iteratively statistical method were implemented to segment the hand radiographs. A dataset containing left hand‐wrist radiographs from 65 referred children was collected. Their results provided 82.49, 83.08, 84.27, 81.76 and 69.04% classification accuracy using the PSO, WB‐PSO, AIW‐PSO, Otsu and the iteratively statistical methods, respectively. To assess the robustness of the implemented methods, white Gaussian noise with different intensities was added to the images and the results indicated that as the noise level increased the robustness against noise for the PSO variants became more highlighted compared to the Otsu and statistical methods. Due to the convincing results, the AIW‐PSO image segmentation system is suggested as an auxiliary diagnostic tool to help specialists for more accurate age bone estimation.