Accurate early diagnosis of malignant skin lesions is crucial in providing adequate and timely treatment. Similarly, early diagnosis of oral cancer is critical as 70% of lesions are presented at their advanced stages. Image-guided tools capable of objectively discriminating cancerous lesions could potentially identify those patients benefiting the most from biopsy examination. Multispectral autofluorescence lifetime (maFLIM)-derived global features can be used in machine-learning models to discriminate malignant from benign pigmented skin lesions. Different pools of global image-level maFLIM features: multispectral intensity, time-domain bi-exponential, and frequency-domain phasor features, were extracted and compared. Further, deep learning models using Long Short-Term Memory (LSTM) networks are developed to differentiate benign and malignant skin lesion as well as healthy and cancerous oral lesions. This pixel-level classifier produced maps to indicate the regions of malignancy. Therefore, the potential of classical machine-learning models as well as deep-learning based LSTM models to differentiate cancerous lesions is demonstrated.