Skin cancer is a significant public health issue, making accurate and early diagnosis crucial. This study proposes a novel and efficient hybrid deep‐learning model for accurate skin cancer diagnosis. The model first employs DeepLabV3+ for precise segmentation of skin lesions in dermoscopic images. Feature extraction is then carried out using three pretrained models: MobileNetV2, EfficientNetB0, and DenseNet201 to ensure balanced performance and robust feature learning. These extracted features are then concatenated, and the ReliefF algorithm is employed to select the most relevant features. Finally, obtained features are classified into eight categories: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, melanocytic nevus, squamous cell carcinoma, and vascular lesion using the kNN algorithm. The proposed model achieves an F1 score of 93.49% and an accuracy of 94.42% on the ISIC‐2019 dataset, surpassing the best individual model, EfficientNetB0, by 1.20%. Furthermore, the evaluation of the PH2 dataset yielded an F1 score of 94.43% and an accuracy of 94.44%, confirming its generalizability. These findings signify the potential of the proposed model as an expedient, accurate, and valuable tool for early skin cancer detection. They also indicate combining different CNN models achieves superior results over the results obtained from individual models.

On constructing a fused image by employing only individual pixels or a set of pixels within a small neighbourhood of the images (SIs) acquired from the same scene, pixel‐based fusion techniques suffer from some drawbacks, such as blurring effects, high sensitivity to noise and misregistration. To overcome these drawbacks, this study proposes a new region‐based image fusion method for thermal and visible images. Since different regions with certain properties need to be emphasised differently in the fused image, the corresponding regions of the SIs are optimally merged to obtain the fused image by employing multiple weighting factors (WFs). To improve the quality of the fused images, WFs were optimised by employing the differential evolution algorithm. Furthermore, a new quality metric was also developed to measure the quality of the fused images during the optimisation process. Experimental results show the feasibility of the proposed method.