Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function

Lyakhov, Pavel; Lyakhova, Ulyana Alekseevna; Kalita, Diana I.

doi:10.20944/preprints202301.0022.v1

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…Unfortunately, such an approach is far from perfect with the level of disagreement between expert interpreters often large. For example, values of disagreement up to 25% noted have been noted in some medical research [ 40 ] and even higher, approximately 30%, in a remote sensing studies [ 41 ]. These disagreements arise because there are many error sources [ 20 , 21 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

Foody

2023

PLoS ONE

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The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in prevalence and the use of an imperfect reference standard. The fundamental issues underlying the problems associated with variations in prevalence and reference standard quality are revisited here for binary classifications with particular attention focused on the use of the Matthews correlation coefficient (MCC). A key attribute claimed of the MCC is that a high value can only be attained when the classification performed well on both classes in a binary classification. However, it is shown here that the apparent magnitude of a set of popular accuracy metrics used in fields such as computer science medicine and environmental science (Recall, Precision, Specificity, Negative Predictive Value, J, F1, likelihood ratios and MCC) and one key attribute (prevalence) were all influenced greatly by variations in prevalence and use of an imperfect reference standard. Simulations using realistic values for data quality in applications such as remote sensing showed each metric varied over the range of possible prevalence and at differing levels of reference standard quality. The direction and magnitude of accuracy metric mis-estimation were a function of prevalence and the size and nature of the imperfections in the reference standard. It was evident that the apparent MCC could be substantially under- or over-estimated. Additionally, a high apparent MCC arose from an unquestionably poor classification. As with some other metrics of accuracy, the utility of the MCC may be overstated and apparent values need to be interpreted with caution. Apparent accuracy and prevalence values can be mis-leading and calls for the issues to be recognised and addressed should be heeded.

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Section: Introductionmentioning

confidence: 99%

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

Foody

2023

PLoS ONE

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Landslide susceptibility assessment using deep learning considering unbalanced samples distribution

Mwakapesa,

Lan,

Mao

2024

Heliyon

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A Hybrid Method for Identifying the Feeding Behavior of Tilapia

et al. 2024

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Accurate feeding control in aquaculture using fish feeding behavior state is key to improving feed utilization efficiency and reducing water pollution. A hybrid method based on ResNet34-CBAM and migration learning is proposed in this paper to achieve tilapia feeding behavior classification. In order to enhance the diversity of samples, pre-processing, such as scaling and cropping, and adding noise, was performed on the images. An underwater camera was used for taking pictures of tilapia behavior, followed by feature extraction using a pre-trained ResNet34 residual network, training with an improved classification for identifying feeding behavior. The testing findings demonstrate that the suggested technique in this article is 99.72% accurate in an actual environment. The model also outperforms other recognition models based on MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5, and ResNet regarding accuracy, precision, and recall compared with other convolutional neural networks. Compared to the original model, the method shows a 7.84 percent improvement, indicating that it can correctly identify the feeding behavior of real fish in aquaculture and then scientifically determine the amount of baiting, which can improve feed utilization, increase aquaculture production, and decrease aquaculture costs.

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Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function

Cited by 3 publications

References 75 publications

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

Landslide susceptibility assessment using deep learning considering unbalanced samples distribution

A Hybrid Method for Identifying the Feeding Behavior of Tilapia

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