Skin　malignant　melanoma　is　a　high-risk　tumor　with　low　incidence　but　high　mortality　rates.　Early　detection　and　treatment　are　crucial　for　a　cure.　Machine　learning　studies　have　focused　on　classifying　melanoma　tumors,　but　these　methods　are　cumbersome　and　fail　to　extract　deeper　features.　This　limits　their　ability　to　distinguish　subtle　variations　in　skin　lesions　accurately,　hindering　effective　early　diagnosis.　The　study　introduces　a　deep　learning-based　network　specifically　designed　for　skin　lesion　detection　to　enhance　data　in　the　melanoma　dataset.　It　leverages　a　novel　FCDS-CNN　architecture　to　address　class-imbalanced　problems　and　improve　data　quality.　Specifically,　FCDS-CNN　incorporates　data　augmentation　and　class　weighting　techniques　to　mitigate　the　impact　of　imbalanced　classes.　It　also　presents　a　practical,　large-scale　solution　that　allows　seamless,　real-world　incorporation　to　support　dermatologists　in　their　early　screening　processes.　The　proposed　robust　model　incorporates　data　augmentation　and　class　weighting　to　improve　performance　across　all　lesions.　The　proposed　dataset　includes　10015　images　of　seven　classes　of　skin　lesions　available　in　Kaggle.　To　overcome　the　dominance　of　one　class　over　the　other,　methods　like　data　augmentation　and　class　weighting　are　used.　The　FCDS-CNN　showed　improved　accuracy　with　an　average　accuracy　of　96%,　outperforming　pre-trained　models　such　as　ResNet,　EfficientNet,　Inception,　and　MobileNet　in　the　precision,　recall,　F1-score,　and　area　under　the　curve　parameters.　These　pre-trained　models　are　more　effective　for　general　image　classification　and　struggle　with　the　nuanced　features　and　class　imbalances　inherent　in　medical　image　datasets.　The　FCDS-CNN　demonstrated　practical　effectiveness　by　outperforming　the　compared　pre-trained　model　based　on　distinct　parameters.　This　work　is　a　testament　to　the　importance　of　specificity　in　medical　image　analysis　regarding　skin　cancer　detection.