博碩士論文 111522601 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系zh_TW
DC.creator本尼zh_TW
DC.creatorBrahma reddy Akumallaen_US
dc.date.accessioned2024-8-1T07:39:07Z
dc.date.available2024-8-1T07:39:07Z
dc.date.issued2024
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111522601
dc.contributor.department資訊工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract深度學習在醫學診斷中的應用,特別是對覆雜的腫塊型乳腺癌,取得了顯著進展。一種采用批量調度器進行動態批量大小調整的新型訓練策略顯示出4\%的性能提升。通過結合注意力機制和雙優化器策略,進一步提高了F1分數和性能的強健性,與傳統方法相比表現更優。此外,一種結合卷積層和變壓器模型的新型特征選擇機制在實驗中表現優於現有的注意力機制。這一創新,加上集成了預訓練權重的“ConvNext Tiny”模型,大大增強了乳腺癌檢測系統的強健性,突顯了這些方法在改進醫學診斷程序中的潛力。zh_TW
dc.description.abstractThe application of deep learning in medical diagnostics, particularly for complex mass-type breast cancers, has seen significant advancements. A novel training strategy that employs a batch scheduler for dynamic batch size adjustment has demonstrated a performance improvement of 4\%. Further enhancements were achieved by incorporating attention mechanisms and dual optimizer strategies, resulting in superior F1 scores and robust performance compared to traditional methods.Additionally, a novel feature selection mechanism combining convolution layers and a transformer model outperformed established attention mechanisms in experimental trials. This innovation, along with the integration of the ′ConvNext Tiny′ model with pre-trained weights, substantially enhanced the robustness of the breast cancer detection system, underscoring the potential of these methodologies for improving medical diagnostic procedures.en_US
DC.subjectConvNextzh_TW
DC.subject變壓器zh_TW
DC.subject卷積層zh_TW
DC.subject注意力zh_TW
DC.subject乳房腫塊zh_TW
DC.subjectConvNexten_US
DC.subjecttransformeren_US
DC.subjectconvolution layersen_US
DC.subjectattentionen_US
DC.subjectBreast massen_US
DC.title提升乳癌篩檢效率之批次排程框架zh_TW
dc.language.isozh-TWzh-TW
DC.titleA Batch Scheduling Framework for Improved Diagnostic Efficiency in Breast Cancer Screeningen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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