Monkeypox, a rare yet emerging viral disease, presents a significant public health challenge due to its potential for human-to-human transmission and clinical resemblance to other skin conditions. Early and accurate detection of Monkeypox is crucial for timely intervention and containment. This research addresses this pressing issue by proposing a modified Inception-v3 deep learning architecture tailored for the detection of Monkeypox from dermatological images. This research is motivated by the importance of early diagnosis in mitigating the impact of Monkeypox outbreaks. The modified Inception-v3 model is customized to capture features specific to Monkeypox skin lesions, enhancing its diagnostic accuracy. Comprehensive experiments were conducted comparing the performance of proposed model against commonly used deep learning architectures, including ResNet, VGG16, and Xception. Experimentation is performed using a real-world dataset. The results demonstrate superiority of proposed model in terms of accuracy, precision, recall, and F1-Score. This research is targeted to contribute to field of medical image processing by addressing the gap in research dedicated to Monkeypox diagnosis.