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Article History

Received: 5 February 2026

Accepted: 28 April 2026

First Online: 21 May 2026

Declarations

:

: This study is based exclusively on publicly available, de-identified histopathological image datasets and simulated computational experiments. It does not involve human participants, animals, clinical interventions, prospective data collection, or access to identifiable patient information. No personal, sensitive, or confidential data were used at any stage of the research. Therefore, formal approval from an institutional ethics committee or review board was not required. The study does not include human subjects, patient recruitment, surveys, interviews, or direct interaction with individuals. All analyses were conducted on publicly available datasets with no identifiable personal information.

: This manuscript does not contain any identifiable personal data, images of human subjects, or patient-specific information. All figures and results are derived from anonymized datasets and algorithmic outputs.

: This research does not involve clinical trials, patient enrollment, medical interventions, or biomedical experimentation. The proposed framework is a computational machine learning and deep learning–based diagnostic support system evaluated using publicly available histopathological image datasets and simulated deployment scenarios.

: The authors declare no competing interests.