Hierarchical confusion matrix for classification performance evaluation

Riehl, Kevin; Neunteufel, Michael; Hemberg, Martin

doi:10.1093/jrsssc/qlad057

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In our evaluation of the models, we employed a range of performance metrics including Area Under the Curve (AUC), F1 score, Accuracy, and Recall ( 31 ). Accuracy score is the ratio of the number of correct predictions and the total number of predictions, while recall score indicates how many of the actual positive cases were predicted correctly with our model.…”

Section: Methodsmentioning

confidence: 99%

An AI based, open access screening tool for early diagnosis of Burkitt lymphoma

Nambiar,

Rajesh,

Nair

et al. 2024

Front. Med.

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Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis of BL contributes to high mortality in BL endemic regions of Africa. Lack of enough pathologists in the region is a major reason for delayed diagnosis. The work described in this paper is a proof-of-concept study to develop a targeted, open access AI tool for screening of histopathology slides in suspected BL cases. Slides were obtained from a total of 90 BL patients. 70 Tonsillectomy samples were used as controls. We fine-tuned 6 pre-trained models and evaluated the performance of all 6 models across different configurations. An ensemble-based consensus approach ensured a balanced and robust classification. The tool applies novel features to BL diagnosis including use of multiple image magnifications, thus enabling use of different magnifications of images based on the microscope/scanner available in remote clinics, composite scoring of multiple models and utilizing MIL with weak labeling and image augmentation, enabling use of relatively low sample size to achieve good performance on the inference set. The open access model allows free access to the AI tool from anywhere with an internet connection. The ultimate aim of this work is making pathology services accessible, efficient and timely in remote clinics in regions where BL is endemic. New generation of low-cost slide scanners/microscopes is expected to make slide images available immediately for the AI tool for screening and thus accelerate diagnosis by pathologists available locally or online.

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Section: Methodsmentioning

confidence: 99%

An AI based, open access screening tool for early diagnosis of Burkitt lymphoma

Nambiar,

Rajesh,

Nair

et al. 2024

Front. Med.

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Enhancing Bank Customer Protection Against Phishing Attacks Through Xgboost-Based Feature Analysis

Karin,

Sani,

Alzami

et al. 2024

Transmisi J.Ilm. Tek. Elekt.

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Internet usage in Indonesia has significantly increased, with approximately 175.4 million people or 64% of the population actively using the internet. While the internet provides numerous benefits, such as easy access to information and faster communication, this rise in usage also opens opportunities for cybercriminals to exploit user vulnerabilities. One of the most common forms of cybercrime is phishing, which attempts to steal users' personal information by impersonating a trusted entity. Current methods for detecting phishing are ineffective against zero-day phishing attacks. Therefore, this study employs the XGBoost algorithm to detect phishing websites. The results show that the XGBoost model, using feature selection techniques, can enhance phishing detection accuracy to 95.5%, with a precision of 95.5%, recall of 95.1%, and F1-score of 95.3%. With these capabilities, XGBoost can be used to protect internet users from evolving phishing threats and assist banks in anticipating customer losses.

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Research on Vehicle Pose Detection Method Based on a Roadside Unit

Ni,

Kong,

Yan

et al. 2024

Sensors

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Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.

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Hierarchical confusion matrix for classification performance evaluation

Cited by 3 publications

References 21 publications

An AI based, open access screening tool for early diagnosis of Burkitt lymphoma

An AI based, open access screening tool for early diagnosis of Burkitt lymphoma

Enhancing Bank Customer Protection Against Phishing Attacks Through Xgboost-Based Feature Analysis

Research on Vehicle Pose Detection Method Based on a Roadside Unit

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