Early Detection of Lung Cancer Using CNN Models and Medical Imaging

Early detection of lung cancer proves essential for survival rate improvement because this disease remains the major global cause of cancer death. The research investigates a CNN-based automatic diagnostic system that uses MRI images for early-stage lung cancer diagnosis. The Lung Cancer MRI Images dataset obtained from Kaggle received preprocessing treatment that involved both normalization and data augmentation to boost generalization abilities. The customized CNN network reached its best training accuracy at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5. 4 4 \%}$</tex> and its validation accuracy at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0. 5 0 \%}$</tex>. The model demonstrates strong proficiency for nodule detection as proven by its evaluation metrics which returned precision of 93.10 % recall of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 8. 4 0 \%}$</tex> and F1-score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0. 7 0 \%}$</tex>. Medical application of this model appears feasible, so it stands ready to improve both accuracy and efficiency in lung cancer diagnosis.