Optics and Precision Engineering, Volume. 32, Issue 24, 3644(2024)

Dermoscopic image classification based on multi-scale and three-dimensional interaction feature optimization

Di WANG1... Xiaoqi LÜ1,2,* and Jing LI1 |Show fewer author(s)
Author Affiliations
  • 1School of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology,Baotou0400, China
  • 2School of Information Engineering, Inner Mongolia University of Technology,Hohhot010051, China
  • show less
    References(33)

    [1] G ARORA, A K DUBEY, Z A JAFFERY et al. A comparative study of fourteen deep learning networks for multi skin lesion classification (MSLC) on unbalanced data. Neural Computing and Applications, 35, 7989-8015(2023).

    [2] X M LAN, Y Q JIANG. Application of artificial intelligence in the diagnosis and treatment of melanoma. The Chinese Journal of Dermatovenereology, 37, 855-860(2023).

         兰雪梅, 姜祎群. 人工智能在黑素瘤诊疗中的应用. 中国皮肤性病学杂志, 37, 855-860(2023).

    [3] G ARGENZIANO, H P SOYER. Dermoscopy of pigmented skin lesions: a valuable tool for early diagnosis of melanoma. The Lancet Oncology, 2, 443-449(2001).

    [4] D X LI, F J YANG, Y LIU et al. Skin lesion segmentation network with cross-attention coding. Opt. Precision Eng., 32, 609-621(2024).

         李大湘, 杨福杰, 刘颖. 融入交叉注意力编码的皮肤病变分割网络. 光学 精密工程, 32, 609-621(2024).

    [5] 宋建丽, 吕晓琪, 谷宇. 语义流引导采样结合注意力机制的脑肿瘤图像分割. 光学 精密工程, 32, 565-577(2024).

         J L SONG, X Q LÜ, Y GU. Brain tumor image segmentation based on semantic flow guided sampling and attention mechanism. Opt. Precision Eng., 32, 565-577(2024).

    [6] Y Y GU, Z Y GE, C P BONNINGTON et al. Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE Journal of Biomedical and Health Informatics, 24, 1379-1393(2020).

    [7] S P GODLIN JASIL, V ULAGAMUTHALVI. Skin lesion classification using pre-trained densenet201 deep neural network, 393-396(2021).

    [8] S K SINGH, V ABOLGHASEMI, M H ANISI. Skin cancer diagnosis based on neutrosophic features with a deep neural network. Sensors, 22, 6261(2022).

    [9] 李建威, 吕晓琪, 谷宇. 基于改进ConvNeXt的皮肤镜图像分类方法. 计算机工程, 49, 239-246, 254(2023).

         J W LI, X Q LÜ, Y GU. Dermoscopy image classification method based on improved ConvNeXt. Computer Engineering, 49, 239-246, 254(2023).

    [10] J X ZHUANG, J B CAI, J G ZHANG et al. Class attention to regions of lesion for imbalanced medical image recognition. Neurocomputing, 555, 126577(2023).

    [11] I S A ABDELHALIM, M F MOHAMED, Y B MAHDY. Data augmentation for skin lesion using self-attention based progressive generative adversarial network. Expert Systems with Applications, 165, 113922(2021).

    [12] H ZHANG, C R WU, Z Y ZHANG et al. ResNeSt: split-attention networks, 2735-2745(2022).

    [13] L SUN, J X DONG, J H TANG et al. Spatially-adaptive feature modulation for efficient image super-resolution, 13144-13153(2023).

    [14] J R CHEN, S H KAO, H HE et al. Run, Don't walk: chasing higher flops for faster neural networks, 12021-12031(2023).

    [15] D MISRA, T NALAMADA, A U ARASANIPALAI et al. Rotate to attend: convolutional triplet attention module, 3138-3147(2021).

    [17] Y F ZHANG, W FENG, Z Y WU et al. Deep-learning model of ResNet combined with CBAM for malignant-benign pulmonary nodules classification on computed tomography images. Medicina, 59, 1088(2023).

    [20] Q D LIU, L Q YU, L Y LUO et al. Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Transactions on Medical Imaging, 39, 3429-3440(2020).

    [21] S K DATTA, M A SHAIKH, S N SRIHARI et al. Soft attention improves skin cancer classification performance, 27, 13-23(2021).

    [22] G CAI, Y ZHU, Y WU et al. A multimodal transformer to fuse images and metadata for skin disease classification. The Visual Computer, 39, 2781-2793(2023).

    [23] G YANG, S H LUO, P GREER. A novel vision transformer model for skin cancer classification. Neural Processing Letters, 55, 9335-9351(2023).

    [24] J P ZHANG, Y T XIE, Y XIA et al. Attention residual learning for skin lesion classification. IEEE Transactions on Medical Imaging, 38, 2092-2103(2019).

    [25] M A AL-MASNI, D H KIM, T S KIM. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer Methods and Programs in Biomedicine, 190, 105351(2020).

    [26] S KHOULOUD, M AHLEM, T FADEL et al. W-net and inception residual network for skin lesion segmentation and classification. Applied Intelligence, 52, 3976-3994(2022).

    [28] Y Q YAN, J KAWAHARA, G HAMARNEH. Melanoma recognition via visual attention, 793-804(2019).

    [116] N GUO, M Y JIANG, L J GAO et al. Simam: a simple, parameter-free attention module for convolutional neural networks, 11863-11874(2021).

    Tools

    Get Citation

    Copy Citation Text

    Di WANG, Xiaoqi LÜ, Jing LI. Dermoscopic image classification based on multi-scale and three-dimensional interaction feature optimization[J]. Optics and Precision Engineering, 2024, 32(24): 3644

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Apr. 3, 2024

    Accepted: --

    Published Online: Mar. 11, 2025

    The Author Email: LÜ Xiaoqi (lxiaoqi@imut.edu.cn)

    DOI:10.37188/OPE.20243224.3644

    Topics