Lung and colon (LC) cancers are among the most deadly types of cancer, often resulting in death. However, the chances of survival significantly increase with the early detection of these cancers. This study addresses the challenge of early detection of LC tumors, which is crucial for effective treatment. LC cancer is one of the most common tumors that require early detection. However, these tumors appear similar in their early stages, making it challenging for doctors to differentiate between them. To overcome this challenge, we propose an AI-based diagnostic model for detecting LC tumors. Artificial intelligence (AI) techniques have been employed to address this challenge. This study proposes two histopathological image analysis strategies for the early detection of LC cancer. The first strategy involves the diagnosis of LC cancer using decision tree (DT) and random forest (RF) networks with CNN model features, namely ResNet50, DenseNet169, and MobileNet, based on geometric active contour (GAC) and ant colony optimization (ACO) algorithms. The second strategy involves the diagnosis of LC cancer using DT and RF classifiers with the features of combined CNN, namely ResNet50-DenseNet169, DenseNet169-MobileNet, DenseNet169-MobileNet, and ResNet50-DenseNet169-MobileNet, based on the GAC and ACO algorithms. All strategies have demonstrated good results for early stage LC cancer detection. The RF network, which utilized the combined features extracted from the ResNet50- MobileNet-DenseNet169 models, demonstrated notable performance with an AUC of 99.7%, sensitivity of 99.72%, accuracy of 99.8%, precision of 99.76%, and specificity of 99.78%.

Skin cancer, particularly melanoma, is an aggressive form of cancer. The problem with human diagnosis is that it is too subjective and inefficient for detecting early signs. Artificial intelligence (AI) is a solution that can clearly be a very efficient, fast, objective, and accurate pathway to improve early interventions and patient care. A new hybrid model, MobileNetV2-ResNet101-ViTE, was developed, which effectively combines Convolutional Neural Network and the Vision Transformer Encoder (ViTE) using a Spatial Detail Enhancement Block. The proposed model differs from existing models by retaining accurate local histological representations and utilizing the global context necessary for accurate classification of skin lesions. Model performance was assessed on the ISIC2019 dataset, yielding an average Area Under the ROC Curve (AUC) of 96.94% and a classification accuracy of 98%. The model’s high scores in sensitivity to the malignant classes have astounding potential in improving false negatives with scores of 98.5% for Melanoma, and 91.5% for squamous cell carcinoma, giving some confidence in its potential to improve false negatives, and has consistently reported very high specificity (average: 99.55%) and precision (average: 94.44%). The proposed MobileNetV2-ResNet101-ViTE model is ground-breaking for dermatologists using an AI-driven approach for diagnosis and should provide a better outcome for early melanoma detection.