Scalp Disease Detection Using Deep Learning: A Hybrid Ensemble Approach with EfficientNetB0, DenseNet121 and MobileNetV2

Most people at some point deal with scalp problems like flaky skin, thick patches, or hair loss - issues that go beyond looks and touch daily comfort. Spotting these early helps avoid worse outcomes, yet usual diagnosis leans heavily on specialists, which slows things down and brings mixed results. Machines now assist medicine more closely, especially through pattern-reading systems built to interpret visuals. One method called CNN has turned out useful when handling complex images, picking up details human eyes might skip. Here, a model combining several such smart networks works together to sort different scalp conditions without manual input. Starting with EfficientNetB0, the method brings in DenseNet121 alongside MobileNetV2 through transfer learning, pulling from existing knowledge to boost how features are captured. Instead of relying on one model, it blends their outputs by way of a soft voting mechanism - this smooths out errors and strengthens prediction consistency. When tested, the combined setup beats each standalone network, notably where data varies widely or comes in small amounts. Built for realworld use, the framework runs efficiently at scale, supporting medical choices while expanding reach to early detection, even where tools and staff run short.