Detects effective deep learning based breast cancer using lite fine-tune convolution neural network

Breast cancer maintains its position as a major worldwide health issue because we need enhanced diagnostic procedures which detect breast cancer early while optimizing treatment results. This paper introduces the Lite Fine-Tune Convolutional Neural Network (LFT-CNN) for breast cancer recognition which establishes a trading zone between classification precision and computational performance. Deep learning models normally need substantial training and processing power, but LFT-CNN applies transfer learning with minimum parameter adjustments to optimize convolutional layers in pre-trained networks while focusing refinement on high-level feature elements. The training process becomes faster through this system yet delivers comparable performance results. The model showed exceptional capability in identifying mammography cases by reaching 97% classification accuracy during publicly accessible dataset testing beyond typical CNN architecture levels. The approach successfully detects malignant from benign tumors, so it strengthens diagnostic dependability which helps radiologists make clinical choices. The narrow and streamlined structure of LFT-CNN makes it possible to run this automated breast cancer screening system on limited resource platforms like edge devices and mobile health applications. LFT-CNN demonstrates practical and scalable potential for real-world applications because it achieves high accuracy together with light computing demands. These research outcomes demonstrate how well-designed lightweight deep learning models benefit medical imaging because they enhance early detection methods and improve patient outcomes during breast cancer diagnosis.