In all the disease that have existed in mankind lung cancer has emerged as one of the most fata one time and again. Also, it is one of the most common and contributing to deaths among all the cancers. Cases of lung cancer are increasing rapidly. There are about 70,000 cases per year in India. The disease has a tendency to be asymptomatic mostly in its earlier stages thus making it nearly impossible to detect. That’s why early cancer detection plays an important part in saving lives. An early detection can give a patient a better chance to cure and recover. Technology plays a major role in detecting cancer efficiently. Many researchers have proposed different methods based on their studies. In recent times, to use computer technology to solve this problem, several computer-aided diagnosis (CAD) techniques as well as system have been proposed, developed as well as emerged. Those systems use various Machine learning techniques as well as deep learning techniques, there also have been several methods based off of image processing-based techniques to predict the malignancy level of cancer. Here, in this paper, the aim will be focussed onto list, discuss, compare and analyse several methods in image segmentation, feature extraction as well as various techniques to classify and detect lung cancer in there early stages.

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Background: Epilepsy is one of the prevalent neurological disorders characterized by disrupted synchronization between inhibitory and excitatory neurons. Disturbed membrane potential due to abnormal regulation of neurotransmitters and ion transport across the neural cell membrane significantly contributes to the pathophysiology of epilepsy. Potassium ion channels (KCN) regulate the resting membrane potential and are involved in neuronal excitability. Genetic alterations in the potassium ion channels (KCN) have been reported to result in the enhancement of the release of neurotransmitters, the excitability of neurons, and abnormal rapid firing rate, which lead to epileptic phenotypes, making these ion channels a potential therapeutic target for epilepsy. The aim of this study is to explore the variations reported in different classes of potassium ion channels (KCN) in epilepsy patients, their functional evaluation, and therapeutic strategies to treat epilepsy targeting KCN. Methodology: A review of all the relevant literature was carried out to compile this article. Result: A large number of variations have been reported in different genes encoding various classes of KCN. These genetic alterations in KCN have been shown to be responsible for disrupted firing properties of neurons. Antiepileptic drugs (AEDs) are the main therapeutic strategy to treat epilepsy. Some patients do not respond favorably to the AEDs treatment, resulting in pharmacoresistant epilepsy. Conclusion: Further to address the challenges faced in treating epilepsy, recent approaches like optogenetics, chemogenetics, and genome editing, such as clustered regularly interspaced short palindromic repeats (CRISPR), are emerging as target-specific therapeutic strategies.

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