Background　Pneumonia　and　tuberculosis　are　prevalent　pulmonary　diseases　globally,　each　demanding　specific　care　measures.　However,　distinguishing　between　these　two　conditions　imposes　challenges　due　to　the　high　skill　requirements　for　doctors,　the　impact　of　imaging　positions　and　respiratory　intensity　of　patients,　and　the　associated　high　healthcare　costs,　emphasizing　the　imperative　need　for　intelligent　and　efficient　diagnostic　methods.Method　This　study　aims　to　develop　a　highly　accurate　automatic　diagnosis　and　classification　method　for　various　lung　diseases　(Normal,　Pneumonia,　and　Tuberculosis).　We　propose　a　hybrid　model,　which　is　based　on　the　InceptionV3　architecture,　enhanced　by　introducing　Deepwise　Separable　Convolution　after　the　Inception　modules　and　incorporating　the　Squeeze-and-Excitation　mechanism.　This　architecture　successfully　enables　the　model　to　extract　richer　features　without　significantly　increasing　the　parameter　count　and　computational　workload,　thereby　markedly　improving　the　performance　in　predicting　and　classifying　lung　diseases.　To　objectively　assess　the　proposed　model,　external　testing　and　five-fold　cross-validation　were　conducted.　Additionally,　widely　used　baseline　models　in　the　scholarly　community　were　constructed　for　comparison.Result　In　the　external　testing　phase,　the　our　model　achieved　an　average　accuracy　(ACC)　of　90.48%　and　an　F1-score　(F1)　of　91.44%,　which　is　an　approximate　4%　improvement　over　the　best-performing　baseline　model,　ResNet.　In　the　five-fold　cross-validation,　our　model＇s　average　ACC　and　F1　reached　88.27%　+/-　2.76%　and　89.29%　+/-　2.69%,　respectively,　demonstrating　exceptional　predictive　performance　and　stability.　The　results　indicate　that　our　model　holds　promise　for　deployment　in　clinical　settings　to　assist　in　the　diagnosis　of　lung　diseases,　potentially　reducing　misdiagnosis　rates　and　patient　losses.Conclusion　Utilizing　deep　learning　for　automatic　assistance　in　the　diagnosis　of　pneumonia　and　tuberculosis　holds　clinical　significance　by　enhancing　diagnostic　accuracy,　reducing　healthcare　costs,　enabling　rapid　screening　and　large-scale　detection,　and　facilitating　personalized　treatment　approaches,　thereby　contributing　to　widespread　accessibility　and　improved　healthcare　services　in　the　future.