## 6 Conclusions  
Combating label noise in image databases is currently a topic of high interest for many researchers. The majority of solutions within the literature modify either the DNN architecture itself or the learning method. No solutions investigate the efficiency of noisy label detection.  
Our paper focused on the detection of mislabeled images. We proposed MultiNET, a novel algorithm based on the current state-of-the-art confidence learning algorithm. Two mechanisms of the algorithm are adapted: the use of a set of DNNs, each trained using a different architecture, and the introduction of a \(q\)-quantile value, allowing the algorithm sensitivity to be tuned. Our main idea was as follows:  
Four image databases (CIFAR10, CIFAR100, MNIST, and GTSRB) were used to experimentally validate the effectiveness of MultiNET in detecting label noise. Each dataset contained different types of images and different classification targets. Three different noise types (uniform, class-dependent, and instance-dependent) were introduced to imitate real-world scenarios.  
The experimental results demonstrate the superiority of the MultiNET algorithm when compared to the original confident learning algorithm. First, the MultiNET algorithm achieves a significantly higher ratio of correct detections to false detections. This is particularly noticeable in databases for which classification is somewhat challenging (e.g., CIFAR100). Secondly, MultiNET allows the detection sensitivity to be varied, thus enabling an iterative approach to progressively detecting errors, from the most obvious to the most subtle.  
The proposed algorithm could be a useful tool for the verification of image databases. Specifically, it allows for the automatic verification of large databases in which the labeling may be erroneous due to, for example, crowdsourcing.  
Summarizing, the main research contribution of the work consists of:  
1. A new idea to assemble several classifiers in an algorithm providing a reliable estimation of noisy labels in image datasets;
2. A mechanism for the modification of detection sensitivity;
3. A comprehensive evaluation of the efficiency of finding noisy labels (the previous works concentrated only the final accuracy on the improved network);
4. The utilization of instance-dependent noise--a recently introduced most realistic model of label noise in image databases.  
A natural extension of the algorithm is the use of a voting method in place of the AND operator. For example, a decision about detection certainty could be made if \(N\) of \(M\) networks (\(N<M\)) agreed. This approach could be applied to difficult classification tasks for which the used architectures display highly variable levels of accuracy. Moreover, all the architectures used show very similar levels of accuracy for all tested image databases. The use of a voting method will be considered in future research using more unusual image datasets.  
Conceptualization, methodology, validation, formal analysis, writing and editing: A.P.; validation, formal analysis, writing and editing, supervision: M.S., K.S., K.R. and S.L. All authors have read and agreed to the published version of this manuscript.  
All authors received support from LIDER/51/0221/L-11/19/NCBR/2020, A.P. acknowledges also support from the Polish Ministry of Science and Higher Education funding for statutory activities BK-246/RAu-11/2022.  
Not applicable.  
The data used in this study included, i.e., CIFAR10, CIFAR100, GTSRB and MNIST image sets, are openly available.  
The authors declare no conflict of interest.