Reconstruction of Mandible and Maxilla After Resection for Non-melanoma Skin Cancer or Cutaneous Melanoma

Kolb, F.

doi:10.1007/978-3-030-18797-2_11

Cited by 1 publication

(2 citation statements)

References 307 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BCC is the most prevalent and grows slowly, but SCC grows quickly and can spread. Both are associated with chronic sun exposure and often manifest in sun‐exposed areas such as the face and neck 2 . Early identification of skin cancer is critical, as early detection and treatment greatly improve the chances of a full recovery.…”

Section: Introductionmentioning

confidence: 99%

“…Both are associated with chronic sun exposure and often manifest in sun-exposed areas such as the face and neck. 2 Early identification of skin cancer is critical, as early detection and treatment greatly improve the chances of a full recovery. Consequently, early interventions can substantially lower the mortality rate associated with skin cancer.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture

Devaraj,

Ravi

2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature enhancement branch, and a global feature aggregation branch. We employ transposed convolution layers for upsampling, cross‐channel normalization, and the Swish activation function to enhance feature representations. Channel shuffle operations and group convolutions stimulate cross‐channel information exchange in both branches. The global feature aggregation branch utilizes global average pooling and depth concatenation to combine features from all branches. Subsequent Swish activation, followed by fully connected layers and softmax activation, yields class probabilities for precise skin cancer classification. This multi‐branch framework offers a promising avenue for accurate and informed medical image analysis in skin cancer detection. Integral to this research is the utilization of the ISIC2019 and ISIC 2020 datasets, encompassing diverse dermoscopic images from multiple sources. By leveraging these datasets, the methodology capitalizes on comprehensive data for precise skin cancer detection, thereby advancing medical image analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%