A spatially localized DNA linear classifier (DNA IC-CLA) for cancer diagnosis has been developed, leveraging DNA-based molecular computing to perform efficient linear classification of miRNA inputs. The DNA IC-CLA utilizes a two-dimensional DNA origami framework and localized processing modules to execute arithmetic operations such as multiplication, addition, and subtraction, enabling accurate and rapid cancer diagnosis in synthetic and clinical samples. Compared to traditional freely diffusible DNA circuits, the DNA IC-CLA offers faster and more effective classification, with a processing time of about 3 hours. The system demonstrates high sensitivity and accuracy in classifying miRNA inputs, with the ability to distinguish between cancerous and healthy samples. The DNA IC-CLA is designed with a neuromorphic architecture, allowing it to perform complex computations at the molecular level. It integrates multiple localized computing cores for arithmetic operations and includes reporting zones for classification results. The system is capable of processing multiple inputs simultaneously and has been validated with synthetic and clinical samples, achieving high sensitivity and specificity in diagnosing non-small cell lung cancer (NSCLC). The DNA IC-CLA also shows robustness and stability, maintaining accurate computations even after one week of operation. To enhance the classification accuracy, the DNA IC-CLA was tested with synthetic samples that mimic real clinical samples, demonstrating a high classification accuracy. The system was further validated using clinical serum samples from NSCLC patients and healthy individuals, achieving a sensitivity of 76.0% and specificity of 80.0%. The DNA IC-CLA's performance was compared with traditional DNA-based systems, showing significant improvements in speed and accuracy. The DNA IC-CLA represents a significant advancement in DNA-based molecular computing, offering a powerful tool for in vivo applications and complex disease classification. It integrates automated equipment with molecular computing systems for one-step diagnostics without manual data analysis. The system's ability to process multiple biomarkers and its high computational efficiency make it a promising platform for future developments in DNA-based diagnostics and bio-computing.A spatially localized DNA linear classifier (DNA IC-CLA) for cancer diagnosis has been developed, leveraging DNA-based molecular computing to perform efficient linear classification of miRNA inputs. The DNA IC-CLA utilizes a two-dimensional DNA origami framework and localized processing modules to execute arithmetic operations such as multiplication, addition, and subtraction, enabling accurate and rapid cancer diagnosis in synthetic and clinical samples. Compared to traditional freely diffusible DNA circuits, the DNA IC-CLA offers faster and more effective classification, with a processing time of about 3 hours. The system demonstrates high sensitivity and accuracy in classifying miRNA inputs, with the ability to distinguish between cancerous and healthy samples. The DNA IC-CLA is designed with a neuromorphic architecture, allowing it to perform complex computations at the molecular level. It integrates multiple localized computing cores for arithmetic operations and includes reporting zones for classification results. The system is capable of processing multiple inputs simultaneously and has been validated with synthetic and clinical samples, achieving high sensitivity and specificity in diagnosing non-small cell lung cancer (NSCLC). The DNA IC-CLA also shows robustness and stability, maintaining accurate computations even after one week of operation. To enhance the classification accuracy, the DNA IC-CLA was tested with synthetic samples that mimic real clinical samples, demonstrating a high classification accuracy. The system was further validated using clinical serum samples from NSCLC patients and healthy individuals, achieving a sensitivity of 76.0% and specificity of 80.0%. The DNA IC-CLA's performance was compared with traditional DNA-based systems, showing significant improvements in speed and accuracy. The DNA IC-CLA represents a significant advancement in DNA-based molecular computing, offering a powerful tool for in vivo applications and complex disease classification. It integrates automated equipment with molecular computing systems for one-step diagnostics without manual data analysis. The system's ability to process multiple biomarkers and its high computational efficiency make it a promising platform for future developments in DNA-based diagnostics and bio-computing.