Early breast cancer diagnosis using cogent activation function‐based deep learning implementation on screened mammograms

Rajput, Gunjan; Agrawal, Shashank; Biyani, Kunika Naresh; Vishvakarma, Santosh Kumar

doi:10.1002/ima.22701

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Based on these results, tumors will be accurately diagnosed, staged, and classified to help the patients benefit from precision treatment. AI can play a part in tumor prevention, screening, diagnosis, treatment, and prognosis prediction (7)(8)(9)(10). After AI is injected into the clinical process, it will improve the detection rate of lesions and make the screening method more effective.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence assists precision medicine in cancer treatment

Liao

et al. 2023

Front. Oncol.

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Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence assists precision medicine in cancer treatment

Liao

et al. 2023

Front. Oncol.

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“…Breast cancer is one of the most deadly malignancies among women worldwide [ 1 ]. The difficulty of accurately screening for early-stage tumors has increased mortality from breast cancer [ 2 , 3 ]. Therefore, early identification of breast cancer is necessary for proper treatment and recovery.…”

Section: Introductionmentioning

confidence: 99%

Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer

Wang

Han

et al. 2023

PLoS ONE

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Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.

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