Abstract:
public health problems worldwide. So far, the application of machine learning
algorithms has shown that earlier and more accurate diagnosis is better for patient
health. This article describes the use of his three new CNNs to detect skin cancers,
including melanoma. The dataset used for the study includes dermoscopy photographs
from different datasets ensuring diversity of melanoma and non-melanoma cases. An
extensive training and validation process improved the CNN that differentiates
between benign and malignant diseases. The fact that the accuracy for this model was
amazingly high at 89% percent clearly sets it apart from all of the others.This research
is of great importance to the prediction of the progress in skin cancer diagnosis in the
near future. Machine learning model, such as ResNet50v2 can be used in the healthcare
sector for the early detection and diagnosis of melanoma which will result into changed
healthcare. The high rate of precision in the ResNet50v2 model will aid in early
detection and ultimately improve patient results. Going forward, there are high hopes
that other better screening techniques for early melanoma would become available
especially those involving minimal invasiveness and thus better prognosis and lesser
melanoma-related deaths.
