Massive　annotated　datasets　are　necessary　for　networks　of　deep　learning.　When　a　topic　is　being　researched　for　the　first　time,　as　in　the　situation　of　the　viral　epidemic,　handling　it　with　limited　annotated　datasets　might　be　difficult.　Additionally,　the　datasets　are　quite　unbalanced　in　this　situation,　with　limited　findings　coming　from　significant　instances　of　the　novel　illness.　We　offer　a　technique　that　allows　a　class　balancing　algorithm　to　understand　and　detect　lung　disease　signs　from　chest　X-ray　and　CT　images.　Deep　learning　techniques　are　used　to　train　and　evaluate　images,　enabling　the　extraction　of　basic　visual　attributes.　The　training　objects＇　characteristics,　instances,　categories,　and　relative　data　modeling　are　all　represented　probabilistically.　It　is　possible　to　identify　a　minority　category　in　the　classification　process　by　using　an　imbalance-based　sample　analyzer.　In　order　to　address　the　imbalance　problem,　learning　samples　from　the　minority　class　are　examined.　The　Support　Vector　Machine　(SVM)　is　used　to　categorize　images　in　clustering.　Physicians　and　medical　professionals　can　use　the　CNN　model　to　validate　their　initial　assessments　of　malignant　and　benign　categorization.　The　proposed　technique　for　class　imbalance　(3-Phase　Dynamic　Learning　(3PDL))　and　parallel　CNN　model　(Hybrid　Feature　Fusion　(HFF))　for　multiple　modalities　achieve　a　high　F1　score　of　96.83　and　precision　is　96.87,　its　outstanding　accuracy　and　generalization　suggest　that　it　may　be　utilized　to　create　a　pathologist＇s　help　tool.