Accurate　classification　of　respiratory　abnormality　levels　is　crucial　for　early　detection　and　diagnosis　of　respiratory　diseases,　making　it　a　pivotal　area　in　the　field　of　medical　diagnostics.　This　study　proposes　a　novel　artificial　intelligence　approach　for　accurate　classification　of　respiratory　abnormality　levels.　By　transforming　respiratory　sound　time-series　data　into　image　representations　using　recurrent　plot,　Markov　transition　field,　and　Gramian　angular　field,　we　capture　intricate　temporal　patterns　and　spatial　relationships.　A　deep　neural　network　autonomously　extracts　discriminative　features　from　these　representations,　subsequently　integrated　into　machine　learning　classifiers.　Leveraging　the　International　Conference　on　Biomedical　and　Health　Informatics　(ICBHI)　database,　our　methodology　achieves　remarkable　classification　accuracy　of　100%　for　both　binary　and　four-　class　scenarios,　accurately　distinguishing　normal　from　abnormal　sounds,　and　discriminating　between　crackles,　wheezes,　and　their　combinations.　The　SHapley　Additive　exPlanations　(SHAP)　method　enhances　interpretability,　providing　insights　into　feature　importance　and　decision-making　processes.　This　interpretable　and　high-　performing　approach　offers　significant　promise　for　enhancing　the　accuracy　and　reliability　of　respiratory　disorder　diagnosis　and　treatment　planning　in　clinical　settings,　potentially　improving　patient　outcomes　and　healthcare　efficiency.