2026-10-07T00:11:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34035234 2026-09-30T17:32:37Z category_146 category_12 category_15 category_21 category_734 category_931 category_64 category_106 portal_5 item_type_3 month_year_09_2026

<p>Paired t-test results.</p> Jincan Zhang (2848232) Chuanqi Cai (9927216) XingHua Tan (25145259) Wenna Chen (8691120) Xin Zhao (71840) Ganqin Du (24280674) Hongwei Jiang (402234) Space Science Cell Biology Neuroscience Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Science Policy xlink "> due magnetic resonance imaging guiding treatment decisions also sense direction spatial pyramid pooling position sensitive information gradient vanishing problem model &# 8217 incorporates convdatt block also susceptible pooling layer pooling functions convdatt block information loss complementary information two aspects tumor types training stability substantial impact subjective bias stable training receptive field patient prognosis parameter reduction paper introduces module alleviates manual interpretation mainly reflected invasive nature input data human health high resolution feature extraction effectively extract dimensionality reduction different scales coordinated attention complementary relationship brain tumors accurate recognition <div><p>Due to the substantial impact of brain tumors on human health, precise identification of tumor types is vital for patient prognosis and guiding treatment decisions. Magnetic Resonance Imaging (MRI) technology, owing to its non-invasive nature and high resolution, is indispensable in accurately classifying brain tumor types. However, manual interpretation of MRI images is not only time-consuming and labor-intensive but also susceptible to subjective bias. Hence, many automatic brain tumor diagnosis systems utilizing deep learning techniques have been developed. This paper introduces an advanced modularized Convolutional Neural Network (CNN) that incorporates ConvDAtt block, Complementary Information (CI) downsampling block, and Spatial Pyramid Pooling (SPP) block. Firstly, the ConvDAtt block combines convolution, depth-separable convolution and Coordinated Attention (CA) mechanism. The module alleviates the gradient vanishing problem in deep neural network training, enhancing the training stability and performance of the model. In the ConvDAtt block, the CA mechanism can not only capture information across channels, but also sense direction and position sensitive information, improving the accuracy of the model’s identification of objects of interest. Secondly, the CI block is a downsampling block with convolution and pooling functions. In the CI block, there is a complementary relationship between the pooling layer and the convolution layer, which is mainly reflected in two aspects, feature extraction and dimensionality reduction, enlargement of receptive field. Moreover, the complementary relationship can solve the problem of information loss in the process of downsampling, while also significantly reducing computational cost. Thirdly, the SPP block extracts features from different scales of the input data and integrates these features, thus enhancing the performance of the network, and enhancing the robustness of the model. The combination of ConvDAtt block, CI block and SPP block can effectively extract the information features and discriminant features of brain tumor MRI images, which has the advantages of parameter reduction, stable training and clear structure.</p></div> 2026-09-30T17:32:13Z Dataset Dataset 10.1371/journal.pone.0343457.t005 https://figshare.com/articles/dataset/_p_Paired_t-test_results_p_/34035234 CC BY 4.0