Early and accurate identification of malignant melanoma continues to be a major challenge for clinicians in the field. Traditional diagnostic approaches, including physical examination, histology, imaging, and nodal assessments, are frequently costly, require significant expertise, and can display large variations among clinicians. These factors may result in missed or misdiagnosis, which often significantly affects a patient’s prognosis. We examine in detail how the application of AI methods such as machine learning and deep learning can be used to advance early detection and identification of melanoma. We review various AI algorithms, including standard classifiers, ensemble techniques, and complex deep learning models. Hybrid models that combine convolutional neural networks (CNNs) and support vector machines (SVMs) are emphasized in this review, as they show enhanced performance and improved resistance to variations in the diagnostician’s input. Better utility of transfer learning and data augmentation approaches is discussed to overcome the challenges posed by small and unbalanced medical datasets. The authors consider the combination of various types of medical information for more effective cancer diagnosis. However, significant obstacles, including model explainability, privacy safeguarding, and clinical evaluation, still need to be addressed. Extensive efforts are needed to overcome these barriers if AI systems are to be effectively adopted within healthcare environments. We suggest that AI offers the opportunity to revolutionize melanoma care by enabling rapid decision support and individualized treatment plans. Realizing this opportunity will depend on effective partnerships between researchers, clinicians, and industry to bring together advances in technology and their effective implementation in the healthcare system.

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high F 1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state‐of‐the‐art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine‐tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.

In the global fight against breast cancer, the importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly improves survival rates. Our research introduces an innovative ensemble method that synergistically combines the strengths of four state‐of‐the‐art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These networks were chosen for their architectural advances and proven efficacy in image classification tasks, particularly in medical imaging. Each network within our ensemble is uniquely optimized: EfficientNet is fine‐tuned with customized scaling to address dataset specifics; AlexNet employs a variable dropout mechanism to reduce overfitting; ResNet benefits from learnable weighted skip connections for better gradient flow; and DenseNet uses selective connectivity to balance computational efficiency and feature extraction. This ensembling strategy combines the predictive output of multiple CNNs, each trained with an individually optimized network, to enhance the ensemble’s overall diagnostic performance. This provides higher precision and stability than any model and shows outstanding performance in the early stage of breast cancer with a precision of up to 94.6%, sensitivity of 92.4%, specificity of 96.1%, and area under the curve (AUC) of 98.0%. This ensemble framework indicated a leap in the early diagnosis of breast cancer as it is a powerful tool that combines several state‐of‐the‐art techniques, hence providing better results.