Abstract

Breast cancer, a critical global health concern, necessitates accurate and timely diagnosis. This research introduces a novel methodology that harnesses modern technologies, including deep learning and transfer learning, to enhance breast cancer cell segmentation. The study commences with meticulous dataset selection and preprocessing, followed by image segmentation using advanced techniques to differentiate between benign and malignant cells effectively. Two significant algorithms, Convolutional Neural Networks (CNN) and AlexNet, are employed, achieving remarkable classification accuracy of 94.5

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@article{cai-7377,
  author  = {Nida Khalil and Khalid Mahboob and Umme Laila and Manal A. Asiri and Muhammad Noman Saeed and Ahmad Mufarreh Almufarreh and Fatima Waseem and Khaled Mohammed Noaman and Farooq Ebrahim and Fazal Imam Shahi and Ali Ahmed Al-Makramani},
  title   = {Advancing Early Diagnosis: Investigating Breast Cancer Cell Segmentation with Deep Learning and Transfer Learning Approaches},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {2},
  pages   = {272--299},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_2_272}
}
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