Abstract
Cancer is an abnormal expansion of tissues and is among the common illnesses in India, accounting for 0.3 fatalities each year. It can appear in any way and is incredibly challenging to spot in its early phases. Thus, Speech organ-related cancer detection using image processing-based techniques and ML is a challenging field in the medical domain, which involves early detection and diagnosis of cancer. This research defines the methods, algorithms, and datasets used by the existing researchers on speech organ diseases. The results from the state-of-the-art works are evaluated by accuracy, false-positive rate, and area under the ROC curve (AUC). The merits and demerits of these approaches are examined, which paves the way for future research in reducing the death rate of patients. The literature studies motivate us to develop early detection of cancer of all speech organs with resources of mobile-related applications to use in real-time will be our future vision. Thus, 60-80% of all speech-related organ infections or cancerous cases can be detected at early stages by this one mobile-related application, which will be beneficial for people in reducing the death rate of patients.
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