Accurate classification of breast tumors into benign

The gap

Evidence profile

Sourced from the stated research gap and future-work section of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 5 representative gaps

• Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · International Journal of Information Engineering and Electronic Business · doi

  The absence of effective, scalable, and accessible early breast cancer detection systems is a significant gap. Traditional diagnostic procedures are expensive, time-consuming, and dependent on skilled labor power. There is a need for more research on the application of machine learning in breast cancer diagnosis.

  generalstated research gap
  Keywords: absence effective scalable accessible early breast cancer detection
• Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · International Journal of Information Engineering and Electronic Business · doi

  Future research should focus on developing more effective and generalizable machine learning models for breast cancer diagnosis. The study highlights the need for more research on the application of machine learning in resource-constrained settings. Further research is needed to compare the performance of different machine learning algorithms in breast cancer diagnosis.

  generalfuture-work section
  Keywords: future research focus developing effective generalizable machine learning
• Machine Learning Based Classification Analysis of Benign and Malignant Breast Tumors: A Clinical Perspective (2026) · International Journal of Drug Delivery Technology · doi

  There is a need for accurate classification of breast tumors into benign and malignant groups. The current diagnostic methods have limitations, such as inter-observer variability and human error. There is a need for automated, dependable breast cancer detection systems that promote early intervention.

  generalstated research gapevidence 5/5
  Keywords: there need accurate classification breast tumors benign malignant
• An Improved Early Breast Cancer Cells Classification and Prediction Based on a Fuzzy Neural Network Model (2026) · International Journal for Engineering Modelling · doi

  The current diagnosis process has a potential risk of misdiagnosis due to inherent complexity. There is a need for an artificial intelligence system that can accurately classify breast cancer cells. The study identifies a gap in the existing literature for a reliable and efficient classification system.

  generalstated research gapevidence 5/5
  Keywords: current diagnosis process has potential risk misdiagnosis due
• Machine Learning-Based Diagnosis of Liver Diseases: A Comprehensive Review and Comparative Analysis (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  Future research should focus on addressing challenges such as data imbalance and model interpretability. The development of new strategies and technologies is crucial for better disease management. Further research is needed to explore the application of machine learning techniques in clinical decision support systems.

  generalfuture-work sectionevidence 4/5
  Keywords: future research focus addressing challenges data imbalance model

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