Deep CNN based brain tumor detection in intelligent systems

Gupta, Brij B.; Gaurav, Akshat; Arya, Varsha

doi:10.1016/j.ijin.2023.12.001

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models prove invaluable in tasks such as tumor segmentation, organ delineation, and lesion detection. Furthermore, the utility of deep learning extends across various disease domains (Gupta et al, 2024). For instance, in the realm of mammography, deep learning models excel in the identification and analysis of suspicious regions, potentially indicative of breast cancer (Yoon & Kim, 2021).…”

mentioning

confidence: 99%

Semantic-Based Optimization of Deep Learning for Efficient Real-Time Medical Image Segmentation

Wei,

Liu,

Yao

2024

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

In response to the critical need for advanced solutions in medical imaging segmentation, particularly for real-time applications in diagnostics and treatment planning, this study introduces SM-UNet. This novel deep learning architecture efficiently addresses the challenge of real-time, accurate medical image segmentation by integrating convolutional neural network (CNN) with multilayer perceptron (MLP). The architecture uniquely combines an initial convolutional encoder for detailed feature extraction, MLP module for capturing long-range dependencies, and a decoder that merges global features with high-resolution CNN map. Further optimization is achieved through a tokenization approach, significantly reducing computational demands. Its superior performance is confirmed by evaluations on standard datasets, showing interaction times drastically lower than comparable networks—between 1/6 to 1/10, and 1/25 compared to SOTA models. These advancements underscore SM-UNet's potential as a groundbreaking tool for facilitating real-time, precise medical diagnostics and treatment strategies.

show abstract

mentioning

confidence: 99%