Wrist Crack Classification Using Deep Learning and X-Ray Imaging

Senapati, Biswaranjan; Naeem, Awad Bin; Ghafoor, Muhammad Imran; Gulaxi, Vivek; Almeida, Friban; Anand, Manish Raj; Gollapudi, Saroopya; Jaiswal, Chandra

doi:10.1007/978-3-031-56950-0_6

Lecture Notes in Networks and Systems

2024

DOI: 10.1007/978-3-031-56950-0_6

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Wrist Crack Classification Using Deep Learning and X-Ray Imaging

Biswaranjan Senapati,

Awad Bin Naeem,

Muhammad Imran Ghafoor

et al.

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Cited by 1 publication

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Optimizing Interpretability and Dataset Bias in Modern AI Systems

Hema,

Dwibedi,

Varma

et al. 2024

Cross-Industry AI Applications

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As AI systems become deeply ingrained in societal infrastructures, the need to comprehend their decision-making processes and address potential biases becomes increasingly urgent. This chapter takes a critical approach to the issues of interpretability and dataset bias in contemporary AI systems. The authors thoroughly dissect the implications of these issues and their potential impact on end-users. The chapter presents mitigative strategies, informed by extensive research, to build AI systems that are not only fairer but also more transparent, ensuring equitable service for diverse populations. Interpretability and dataset bias are critical aspects of AI systems, particularly in high-stakes applications like healthcare, criminal justice, and finance. In the study, the authors delve deep into the challenges associated with interpreting the decisions made by complex AI models.

show abstract

Optimizing Interpretability and Dataset Bias in Modern AI Systems

Hema,

Dwibedi,

Varma

et al. 2024

Cross-Industry AI Applications

View full text Add to dashboard Cite

show abstract

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Wrist Crack Classification Using Deep Learning and X-Ray Imaging

Cited by 1 publication

References 18 publications

Optimizing Interpretability and Dataset Bias in Modern AI Systems

Optimizing Interpretability and Dataset Bias in Modern AI Systems

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