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Integrative framework for cancer detection via integro-differential equations using deep learning techniques.

Tanneeru Gopisairam¹, Srinivasarao Thota², Thulasi Bikku³

¹Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.

Scientific Reports

|February 18, 2026

Summary

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This summary is machine-generated.

This study introduces a novel deep learning framework for cancer detection in mammograms. By converting 2D images to 1D signals, it achieves high accuracy in identifying cancerous regions.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate cancer diagnosis from medical images is crucial but challenging.
Deep learning offers promising computational tools for enhancing diagnostic accuracy.

Purpose of the Study:

To develop and evaluate a novel framework for cancer detection in mammograms.
To improve interpretability in computational cancer diagnosis using mathematical models.

Main Methods:

Converting 2D mammograms into 1D signals for feature extraction.
Utilizing a 1D convolutional neural network for image classification.
Incorporating integro-differential equations to model tumor dynamics and intensity variations.

Main Results:

Keywords:

Cancer detection Differential equations Explainable AI Image processing Segmentation Transfer learning

Related Experiment Videos

Achieved 96.4% accuracy in binary classification on INbreast and MIAS datasets.
Demonstrated comparable or superior performance to conventional deep learning baselines.
Highlighted advantages in feature extraction and computational efficiency.

Conclusions:

The proposed framework shows significant potential for accurate and interpretable cancer diagnosis in mammography.
Further research is needed to address limitations such as data dependency and information loss during signal conversion.